# api_taskdetail

author: Johnny R. Zhang;Xiaomei Mi;Gaoyuan Du;Qianyi Sun;Shiqi Wang;Jiaxuan Li;Wenhua Zhou
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14216
pdf_url: https://arxiv.org/pdf/2509.14216
site: arxiv
created_at: 2025-09-18 11:28:45.379355
summary:
  Abstract:Stochastic optimization powers the scalability of modern artificial intelligence, spanning machine learning, deep learning, reinforcement learning, and large language model training. Yet, existing theory remains largely confined to Hilbert spaces, relying on inner-product frameworks and orthogonality. This paradigm fails to capture non-Euclidean settings, such as mirror descent on simplices, Bregman proximal methods for sparse learning, natural gradient descent in information geometry, or Kullback--Leibler-regularized language model training. Unlike Euclidean-based Hilbert-space methods, this approach embraces general Banach spaces. This work introduces a pioneering Banach--Bregman framework for stochastic iterations, establishing Bregman geometry as a foundation for next-generation optimization. It (i) provides a unified template via Bregman projections and Bregman--Fejer monotonicity, encompassing stochastic approximation, mirror descent, natural gradient, adaptive methods, and mirror-prox; (ii) establishes super-relaxations (\lambda > 2) in non-Hilbert settings, enabling flexible geometries and elucidating their acceleration effect; and (iii) delivers convergence theorems spanning almost-sure boundedness to geometric rates, validated on synthetic and real-world tasks. Empirical studies across machine learning (UCI benchmarks), deep learning (e.g., Transformer training), reinforcement learning (actor--critic), and large language models (WikiText-2 with distilGPT-2) show up to 20% faster convergence, reduced variance, and enhanced accuracy over classical baselines. These results position Banach--Bregman geometry as a cornerstone unifying optimization theory and practice across core AI paradigms.

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title:
  A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training


author: Yifan Hu;Jie Yang;Tian Zhou;Peiyuan Liu;Yujin Tang;Rong Jin;Liang Sun
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14181
pdf_url: https://arxiv.org/pdf/2509.14181
site: arxiv
title: Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
created_at: 2025-09-18 11:28:45.379816
summary:
  Abstract:Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because they have shown little performance advantage. We challenge this view and demonstrate that explicit representation alignment can supply critical information that bridges the distributional gap between input histories and future targets. To this end, we introduce TimeAlign, a lightweight, plug-and-play framework that learns auxiliary features via a simple reconstruction task and feeds them back to any base forecaster. Extensive experiments across eight benchmarks verify its superior performance. Further studies indicate that the gains arises primarily from correcting frequency mismatches between historical inputs and future outputs. We also provide a theoretical justification for the effectiveness of TimeAlign in increasing the mutual information between learned representations and predicted targets. As it is architecture-agnostic and incurs negligible overhead, TimeAlign can serve as a general alignment module for modern deep learning time-series forecasting systems. The code is available at this https URL.

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date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14155
pdf_url: https://arxiv.org/pdf/2509.14155
site: arxiv
title: Supervised and Unsupervised Deep Learning Applied to the Majority Vote Model
created_at: 2025-09-18 11:28:45.380263
author:
  J. F. Silva Neto;D. S. M. Alencar;L. T. Brito;G. A. Alves;F. W. S. Lima;A. Macedo-Filho;R. S. Ferreira;T. F. A. Alves

summary:
  Abstract:We employ deep learning techniques to investigate the critical properties of the continuous phase transition in the majority vote model. In addition to deep learning, principal component analysis is utilized to analyze the transition. For supervised learning, dense neural networks are trained on spin configuration data generated via the kinetic Monte Carlo method. Using independently simulated configuration data, the neural network accurately identifies the critical point on both square and triangular lattices. Classical unsupervised learning with principal component analysis reproduces the magnetization and enables estimation of critical exponents, typically obtained via Monte Carlo importance sampling. Furthermore, deep unsupervised learning is performed using variational autoencoders, which reconstruct input spin configurations and generate artificial outputs. The autoencoders detect the phase transition through the loss function, quantifying the preservation of essential data features. We define a correlation function between the real and reconstructed data, and find that this correlation function is universal at the critical point. Variational autoencoders also serve as generative models, producing artificial spin configurations.

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author: Haotian Li;Jianbo Jiao
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14149
pdf_url: https://arxiv.org/pdf/2509.14149
site: arxiv
title: An Exploratory Study on Abstract Images and Visual Representations Learned from Them
created_at: 2025-09-18 11:28:45.380735
summary:
  Abstract:Imagine living in a world composed solely of primitive shapes, could you still recognise familiar objects? Recent studies have shown that abstract images-constructed by primitive shapes-can indeed convey visual semantic information to deep learning models. However, representations obtained from such images often fall short compared to those derived from traditional raster images. In this paper, we study the reasons behind this performance gap and investigate how much high-level semantic content can be captured at different abstraction levels. To this end, we introduce the Hierarchical Abstraction Image Dataset (HAID), a novel data collection that comprises abstract images generated from normal raster images at multiple levels of abstraction. We then train and evaluate conventional vision systems on HAID across various tasks including classification, segmentation, and object detection, providing a comprehensive study between rasterised and abstract image representations. We also discuss if the abstract image can be considered as a potentially effective format for conveying visual semantic information and contributing to vision tasks.

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author: Robiul Islam;Dmitry I. Ignatov;Karl Kaberg;Roman Nabatchikov
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14078
pdf_url: https://arxiv.org/pdf/2509.14078
site: arxiv
created_at: 2025-09-18 11:28:45.381187
summary:
  Abstract:This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands, with Adadelta exhibiting robust performance in cross-model evaluations. Specifically, Adagrad excels in the beta band, while RMSprop achieves superior performance in the gamma band. Conversely, SGD and FTRL exhibit inconsistent performance. Among the models, the CNN demonstrates the second highest accuracy, particularly in capturing spatial features of EEG data. The deep dense network shows competitive performance in learning complex patterns, whereas the shallow three-layer network, sometimes being less accurate, provides computational efficiency. SHAP (Shapley Additive Explanations) plots are employed to identify efficient class prediction, revealing nuanced contributions of EEG frequency bands to model accuracy. Overall, the study highlights the importance of optimizer selection, model architecture, and EEG frequency band analysis in enhancing classifier performance and understanding feature importance in neuroimaging-based classification tasks.

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title:
  Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques


date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14062
pdf_url: https://arxiv.org/pdf/2509.14062
site: arxiv
title: Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation
created_at: 2025-09-18 11:28:45.381656
author:
  Saifur Rahman;Syed Luqman Shah;Salman Khan;Jalal Khan;Muhammad Irfan;Maaz Shafi;Said Muhammad;Fazal Muhammad;Mohammad Shahed Akond

summary:
  Abstract:Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive nature of RIS elements prevents direct channel acquisition, incurring prohibitive pilot overhead, computational complexity, and energy consumption. To address this, we propose a deep learning (DL)-based channel estimation framework that reduces pilot overhead by grouping RIS elements and reconstructing the cascaded channel from partial pilot observations. Furthermore, conventional DL models trained under single-user settings suffer from poor generalization across new user locations and propagation scenarios. We develop a distributed machine learning (DML) strategy in which the BS and users collaboratively train a shared neural network using diverse channel datasets collected across the network, thereby achieving robust generalization. Building on this foundation, we design a hierarchical DML neural architecture that first classifies propagation conditions and then employs scenario-specific feature extraction to further improve estimation accuracy. Simulation results confirm that the proposed framework substantially reduces pilot overhead and complexity while outperforming conventional methods and single-user models in channel estimation accuracy. These results demonstrate the practicality and effectiveness of the proposed approach for 6G RIS-assisted systems.

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author: Weihao Yan;Christoph Brune;Mengwu Guo
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14054
pdf_url: https://arxiv.org/pdf/2509.14054
site: arxiv
title: Physics-based deep kernel learning for parameter estimation in high dimensional PDEs
created_at: 2025-09-18 11:28:45.382107
summary:
  Abstract:Inferring parameters of high-dimensional partial differential equations (PDEs) poses significant computational and inferential challenges, primarily due to the curse of dimensionality and the inherent limitations of traditional numerical methods. This paper introduces a novel two-stage Bayesian framework that synergistically integrates training, physics-based deep kernel learning (DKL) with Hamiltonian Monte Carlo (HMC) to robustly infer unknown PDE parameters and quantify their uncertainties from sparse, exact observations. The first stage leverages physics-based DKL to train a surrogate model, which jointly yields an optimized neural network feature extractor and robust initial estimates for the PDE parameters. In the second stage, with the neural network weights fixed, HMC is employed within a full Bayesian framework to efficiently sample the joint posterior distribution of the kernel hyperparameters and the PDE parameters. Numerical experiments on canonical and high-dimensional inverse PDE problems demonstrate that our framework accurately estimates parameters, provides reliable uncertainty estimates, and effectively addresses challenges of data sparsity and model complexity, offering a robust and scalable tool for diverse scientific and engineering applications.

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author: Samuel Tovey;Julian Hoßbach;Sandro Kuppel;Tobias Ensslen;Jan C. Behrends;Christian Holm
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14029
pdf_url: https://arxiv.org/pdf/2509.14029
site: arxiv
title: Deep Learning-Driven Peptide Classification in Biological Nanopores
created_at: 2025-09-18 11:28:45.382546
summary:
  Abstract:A device capable of performing real time classification of proteins in a clinical setting would allow for inexpensive and rapid disease diagnosis. One such candidate for this technology are nanopore devices. These devices work by measuring a current signal that arises when a protein or peptide enters a nanometer-length-scale pore. Should this current be uniquely related to the structure of the peptide and its interactions with the pore, the signals can be used to perform identification. While such a method would allow for real time identification of peptides and proteins in a clinical setting, to date, the complexities of these signals limit their accuracy. In this work, we tackle the issue of classification by converting the current signals into scaleogram images via wavelet transforms, capturing amplitude, frequency, and time information in a modality well-suited to machine learning algorithms. When tested on 42 peptides, our method achieved a classification accuracy of ~81\,\%, setting a new state-of-the-art in the field and taking a step toward practical peptide/protein diagnostics at the point of care. In addition, we demonstrate model transfer techniques that will be critical when deploying these models into real hardware, paving the way to a new method for real-time disease diagnosis.

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date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14016
pdf_url: https://arxiv.org/pdf/2509.14016
site: arxiv
title: Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping
created_at: 2025-09-18 11:28:45.383017
author:
  Jonas Buchli;Brendan Tracey;Tomislav Andric;Christopher Wipf;Yu Him Justin Chiu;Matthias Lochbrunner;Craig Donner;Rana X. Adhikari;Jan Harms;Iain Barr;Roland Hafner;Andrea Huber;Abbas Abdolmaleki;Charlie Beattie;Joseph Betzwieser;Serkan Cabi;Jonas Degrave;Yuzhu Dong;Leslie Fritz;Anchal Gupta;Oliver Groth;Sandy Huang;Tamara Norman;Hannah Openshaw;Jameson Rollins

summary:
  Abstract:Improved low-frequency sensitivity of gravitational wave observatories would unlock study of intermediate-mass black hole mergers, binary black hole eccentricity, and provide early warnings for multi-messenger observations of binary neutron star mergers. Today's mirror stabilization control injects harmful noise, constituting a major obstacle to sensitivity improvements. We eliminated this noise through Deep Loop Shaping, a reinforcement learning method using frequency domain rewards. We proved our methodology on the LIGO Livingston Observatory (LLO). Our controller reduced control noise in the 10--30Hz band by over 30x, and up to 100x in sub-bands surpassing the design goal motivated by the quantum limit. These results highlight the potential of Deep Loop Shaping to improve current and future GW observatories, and more broadly instrumentation and control systems.

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author: Ivana Kesić;Aljaž Blatnik;Carolina Fortuna;Blaž Bertalanič
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14000
pdf_url: https://arxiv.org/pdf/2509.14000
site: arxiv
title: Deep Temporal Graph Networks for Real-Time Correction of GNSS Jamming-Induced Deviations
created_at: 2025-09-18 11:28:45.383462
summary:
  Abstract:Global Navigation Satellite Systems (GNSS) are increasingly disrupted by intentional jamming, degrading availability precisely when positioning and timing must remain operational. We address this by reframing jamming mitigation as dynamic graph regression and introducing a receiver-centric deep temporal graph network that predicts, and thus corrects, the receivers horizontal deviation in real time. At each 1 Hz epoch, the satellite receiver environment is represented as a heterogeneous star graph (receiver center, tracked satellites as leaves) with time varying attributes (e.g., SNR, azimuth, elevation, latitude/longitude). A single layer Heterogeneous Graph ConvLSTM (HeteroGCLSTM) aggregates one hop spatial context and temporal dynamics over a short history to output the 2D deviation vector applied for on the fly correction.

  We evaluate on datasets from two distinct receivers under three jammer profiles, continuous wave (cw), triple tone (cw3), and wideband FM, each exercised at six power levels between -45 and -70 dBm, with 50 repetitions per scenario (prejam/jam/recovery). Against strong multivariate time series baselines (MLP, uniform CNN, and Seq2Point CNN), our model consistently attains the lowest mean absolute error (MAE). At -45 dBm, it achieves 3.64 cm (GP01/cw), 7.74 cm (GP01/cw3), 4.41 cm (ublox/cw), 4.84 cm (ublox/cw3), and 4.82 cm (ublox/FM), improving to 1.65-2.08 cm by -60 to -70 dBm. On mixed mode datasets pooling all powers, MAE is 3.78 cm (GP01) and 4.25 cm (ublox10), outperforming Seq2Point, MLP, and CNN. A split study shows superior data efficiency: with only 10\% training data our approach remains well ahead of baselines (20 cm vs. 36-42 cm).

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author: van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA.
original_link: https://pubmed.ncbi.nlm.nih.gov/35576821/
site: pubmed
title: Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
created_at: 2025-09-18 11:28:45.383902
summary:
  With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

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author: Mousavi SM, Beroza GC.
original_link: https://pubmed.ncbi.nlm.nih.gov/35951699/
site: pubmed
title: Deep-learning seismology.
created_at: 2025-09-18 11:28:45.384365
summary:
  Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep learning to seismology are likely to prove instructive for the geosciences, and perhaps other research areas more broadly. Deep learning is a powerful approach, but there are subtleties and nuances in its application. We present a systematic overview of trends, challenges, and opportunities in applications of deep-learning methods in seismology.

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author: Jiang Y, Yang M, Wang S, Li X, Sun Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/32277744/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7170661/pdf/CAC2-40-154.pdf
site: pubmed
title: Emerging role of deep learning-based artificial intelligence in tumor pathology.
created_at: 2025-09-18 11:28:45.384814
summary:
  The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

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author: Craik A, He Y, Contreras-Vidal JL.
original_link: https://pubmed.ncbi.nlm.nih.gov/30808014/
site: pubmed
title: Deep learning for electroencephalogram (EEG) classification tasks: a review.
created_at: 2025-09-18 11:28:45.385261
summary:
  Objective: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks?

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author: Stahlschmidt SR, Ulfenborg B, Synnergren J.
original_link: https://pubmed.ncbi.nlm.nih.gov/35089332/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8921642/pdf/bbab569.pdf
site: pubmed
title: Multimodal deep learning for biomedical data fusion: a review.
created_at: 2025-09-18 11:28:45.385723
summary:
  Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. Therefore, we review the current state-of-the-art of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for biomedical applications, as well as research on novel methods. By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches. Additionally, the proposed subcategories of fusion strategies show different advantages and drawbacks. The review of current methods has shown that, especially for intermediate fusion strategies, joint representation learning is the preferred approach as it effectively models the complex interactions of different levels of biological organization. Finally, we note that gradual fusion, based on prior biological knowledge or on search strategies, is a promising future research path. Similarly, utilizing transfer learning might overcome sample size limitations of multimodal data sets. As these data sets become increasingly available, multimodal DL approaches present the opportunity to train holistic models that can learn the complex regulatory dynamics behind health and disease.

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author: Zhu Y, Wang M, Yin X, Zhang J, Meijering E, Hu J.
original_link: https://pubmed.ncbi.nlm.nih.gov/36616657/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9823653/pdf/sensors-23-00062.pdf
site: pubmed
title: Deep Learning in Diverse Intelligent Sensor Based Systems.
created_at: 2025-09-18 11:28:45.386168
summary:
  Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

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author: Alvi M, Batstone D, Mbamba CK, Keymer P, French T, Ward A, Dwyer J, Cardell-Oliver R.
original_link: https://pubmed.ncbi.nlm.nih.gov/37716298/
site: pubmed
title: Deep learning in wastewater treatment: a critical review.
created_at: 2025-09-18 11:28:45.386601
summary:
  Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems.

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author: Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J.
original_link: https://pubmed.ncbi.nlm.nih.gov/36560157/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9784550/pdf/sensors-22-09790.pdf
site: pubmed
title: Deep Learning in Medical Hyperspectral Images: A Review.
created_at: 2025-09-18 11:28:45.387061
summary:
  With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.

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author: Li Z, Jiang M, Wang S, Zhang S.
original_link: https://pubmed.ncbi.nlm.nih.gov/36167282/
site: pubmed
title: Deep learning methods for molecular representation and property prediction.
created_at: 2025-09-18 11:28:45.387506
summary:
  With advances in artificial intelligence (AI) methods, computer-aided drug design (CADD) has developed rapidly in recent years. Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contemporary applications of deep learning (DL) methods for molecular representation and property prediction. We categorize DL methods according to the format of molecular data (1D, 2D, and 3D). In addition, we discuss some common DL models, such as ensemble learning and transfer learning, and analyze the interpretability methods for these models. We also highlight the challenges and opportunities of DL methods for molecular representation and property prediction.

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author: Ding W, Nakai K, Gong H.
original_link: https://pubmed.ncbi.nlm.nih.gov/35348602/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9116377/pdf/bbac102.pdf
site: pubmed
title: Protein design via deep learning.
created_at: 2025-09-18 11:28:45.387964
summary:
  Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

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author: 刘晗;陈利军
date: 2025-09-17
keywords: 知识图谱;;思想政治理论课;;教学创新;
source: 濮阳职业技术学院学报
site: 知网
title: 基于知识图谱推动下高校思政课教学的创新研究
created_at: 2025-09-18 13:12:59.092073
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DQBEh_7MNommothEtV8PRxqgjSSFGmWfnHJyE_ZpZEY8spOtlWfagCPmM8o2RlodBJ-UCLot95yn03_wc3JME5DtzUETf5odsyGHu6oE65B2MCf4-5DBpv40RMf-BN3kwbTtg-AOxkYkeKw6BazPOG4iVDJF6sIgl4QcbOsY-fw4w==&uniplatform=NZKPT&language=CHS

summary:
  通过知识图谱技术构建高校思想政治理论课知识图谱，创设高校思政课教学新模式，是信息化技术运用到实际教学过程中的新探索。依托思想政治理论课知识图谱的构建，可以实现线上线下教学的有机融合和深度融合，推动思政课知识体系的可视化建设，实现优质思政资源共建共享，提高思政资源匹配度，打造思政课数字化与智能化的授课环境等。整合思政元素核心内容，可以提升教师数字化素养，推动教学模式转变。

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author: 周仲礼;柳炳利;龚成杰;曹昌杰;孔韫辉
date: 2025-09-15 16:45
keywords: 矿产勘查;;知识图谱;;图神经网络;;本体建模;;知识推理;
source: 成都理工大学学报(自然科学版)
site: 知网
title: 矿产勘查知识图谱——研究进展、关键问题与未来展望
created_at: 2025-09-18 13:12:59.094908
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DT6_VQ-LXYrWWqDgWl7OVSiWrGMV9s-T-nzz2z4vPKlmnc1tK807WVLXRQJPQZr24cZi4bfGEgDPNt0gf8MQIzV-R5HC5fB3LYioApCYnG3GTC74pQi8U6K3V2j3Wix4-qQJAVhE0ZXSx8T54-daa8lhXYlZut4LvuUF7sJSDMUuw==&uniplatform=NZKPT&language=CHS

summary:
  随着大数据与人工智能在地球科学中的广泛应用，知识图谱逐渐成为整合多源地学数据、显式表述成矿过程并支撑找矿靶区预测的重要手段。矿产勘查作为高度依赖因果链推理与专家经验的复杂任务，亟需面向成矿过程的可计算知识表达与推理框架。本文系统梳理矿产勘查知识图谱的研究进展，围绕本体建模、语义融合与推理机制展开综述，并重点评述图神经网络在图结构表示与找矿靶区预测中的应用路径与挑战。知识图谱在因果链建模与可解释预测方面具有明显优势，但在统一语义本体、跨模态数据融合、模型可解释性与工程化部署等方面仍存在瓶颈。此外，当前研究仍严重依赖专家经验，缺乏统一语义本体和稳健的跨模态融合框架，这在一定程度上限制了知识图谱在实际找矿中的广泛应用。面向未来，建议推进“知识-数据-模型”的深度耦合，探索符号逻辑、神经网络与大语言模型的协同推理，并构建面向矿产勘查的通用知识图谱与服务化平台，为构建下一代智能勘查系统提供方法论支撑和实践参考。本文有助于厘清矿产勘查知识图谱的研究脉络，凝练关键问题与发展趋势，为相关技术体系的构建与演进提供理论支撑与路径参考。

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author: 周思俊;张昊;王国明
date: 2025年19期
keywords: 知识图谱;知识图谱构建;知识推理;深度学习;应用框架
quote: 0
source: 《电脑知识与技术》
site: 万方
title: 知识图谱研究综述
created_at: 2025-09-18 13:12:59.095454
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISGWRuenN5anMtaXRyenlrc2IyMDI1MTkwMTAaCHRxZHVhdnB5

summary:
  随着信息时代数据量的爆炸性增长,传统知识库已难以满足目前的数据存储需求.知识图谱通过整合多元数据并以结构化方式展现,能有效应对大数据挑战,因此成为人工智能领域的研究热点.文章首先概述知识图谱的基础理论,其次从三个关键环节探讨构建知识图谱的技术细节及模型优劣,在此基础上提出一套系统的技术路径,最后通过提出两个应用框架凸显知识图谱在实际应用中的优势.文章旨在帮助研究者掌握知识图谱的关键技术,为推动知识图谱的发展提供坚实的理论基础.

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author: 李雯昕;昝红英;关同峰
date: 2025年3期
keywords: 知识图谱;命名实体识别;关系抽取;期货文本
quote: 0
source: 《中文信息学报》
site: 万方
title: 期货领域知识图谱构建
created_at: 2025-09-18 13:12:59.095939
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p3eHh4YjIwMjUwMzAwMhoIdHFkdWF2cHk%3D

summary:
  期货领域是数据最丰富的领域之一,该文以商品期货的研究报告为数据来源构建了期货领域知识图谱(Commodity Futures Knowledge Graph,CFKG).以期货产品为核心,确立了概念分类体系及关系描述体系,形成图谱的概念层;在MHS-BIA与GPN模型的基础上,通过领域专家指导对242万字的研报文本进行标注与校对,形成了CFKG数据层,并设计了可视化查询系统.该文所构建的CFKG包含17 003个农产品期货关系三元组、13703种非农产品期货关系三元组,为期货领域文本分析、舆情监控和推理决策等应用提供知识支持.

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author: 刘启恒;李雪莲;孙孟娜
keywords: CiteSpace与SciExplorer-LDGAS;知识图谱;网络空间命运共同体;文献计量分析;网络安全;人工智能技术
source: 南京师大学报(自然科学版)
site: 维普
title: 跨平台工具链探索网络安全知识图谱研究进展与趋势
created_at: 2025-09-18 13:12:59.096402
original_link:
  https://www.cqvip.com/doc/journal/3508551509?sign=d68652da0f76c80c60fbae1349db54f5e302f4f3c19224ad1b4c91df2ba1adf9&expireTime=1789708104257&resourceId=3508551509

summary:
  在数字化时代,网络安全已成为全球性挑战.网络攻击手段的日益复杂和多变对网络安全领域的研究提出了更高的要求.本研究基于文献计量方法,融合CiteSpace与SciExplorer-LDGAS工具链,对2011—2024年间的343篇中英文文献(含CNKI中文核心文献84篇、Web of Science核心英文文献259篇)进行系统性分析,探究网络安全知识图谱领域的研究趋势、合作网络及热点演变.研究结果表明:(1)该领域发文量自2017年起显著增长,2023—2024年达到峰值,反映计算机技术的发展与学术关注度的增强;(2)作者与机构合作呈现区域性聚集特征,中美两国研究贡献领先(分别占比27%和25%),欧美国家合作较多,但其余跨国合作网络稀疏;(3)关键词分析揭示国内外研究侧重差异:国内聚焦技术突破,国外则侧重应用优化;中英文文献也呈现出显著趋同性,具体聚焦于网络威胁情报、本体构建以及人工智能(AI)等核心方向;(4)研究热点从早期“态势感知”“可视分析”向“知识图谱构建”“攻击模式预测”演进,凸显人工智能技术驱动的防御体系升级需求.基于此,本文提出需加强跨国、跨机构协作,建立统一的知识共享平台,推动知识图谱技术与多源威胁情报的深度整合,为构建网络空间命运共同体提供理论与工具支撑.

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author: 牛崇庆;卢菁;杜钰萱;王劭羽
keywords: 电子病历;医疗时序知识图谱;关系共现图;图卷积网络;演化学习
source: 计算机应用研究
site: 维普
title: 基于演化学习的医疗时序知识图谱链接预测方法
created_at: 2025-09-18 13:12:59.096885
original_link:
  https://www.cqvip.com/doc/journal/3509971778?sign=7619c918ee2ee91f7e492fbe233874ee460c75aba7b59bdac400c40f19d20e28&expireTime=1789708104257&resourceId=3509971778

summary:
  为提高疾病进展、治疗方案及药物反应等医疗事件的预测性能,辅助医疗决策,从电子病历提取出医疗实体及其相互关系,构建医疗知识图谱并利用链接预测模型实现对医疗事件的预测。然而,现有时序链接预测模型面临未能充分利用事实之间的结构依赖信息、难以有效捕捉实体和关系的动态演化特征等问题,提出了基于演化学习的医疗时序知识图谱链接预测模型MedEvoLP。首先构造关系共现图,利用关系图卷积网络和时序图卷积网络进行递归演化,捕捉并发医疗事实中实体和关系的结构依赖,充分利用医疗知识图谱中丰富的上下文信息。MedEvoLP还将多个相邻时间戳组成为时间单元作为模型的基本演化单元,参与医疗实体嵌入与关系嵌入的动态演化,有效捕捉医疗知识图谱的动态演化特征。在MIMIC-III和DiabetesP中提取的临床记录上与8种基准模型进行了对比实验。实验结果表明,MedEvoLP在多个评估指标上均优于基准模型,例如在MIMIC-III数据集上,MedEvoLP的命中率(hits@1)达到42.37%,证明MedEvoLP模型具有更好的性能。

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author: Ziming Wei;Zichen Kong;Yuan Wang;David Z. Pan;Xiyuan Tang
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.14169
site: arxiv
title: TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits
created_at: 2025-09-18 13:12:59.097338
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author: Zhihao He;Tianyao He;Tieyuan Chen;Yun Xu;Huabin Liu;Chaofan Gan;Gui Zou;Weiyao Lin
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.13161
pdf_url: https://arxiv.org/pdf/2509.13161
site: arxiv
created_at: 2025-09-18 13:12:59.097769
summary:
  Abstract:Despite the prosperity of the video language model, the current pursuit of comprehensive video reasoning is thwarted by the inherent spatio-temporal incompleteness within individual videos, resulting in hallucinations and inaccuracies. A promising solution is to augment the reasoning performance with multiple related videos. However, video tokens are numerous and contain redundant information, so directly feeding the relevant video data into a large language model to enhance responses could be counterproductive. To address this challenge, we propose a multi-video collaborative framework for video language models. For efficient and flexible video representation, we establish a Video Structuring Module to represent the video's knowledge as a spatio-temporal graph. Based on the structured video representation, we design the Graph Fusion Module to fuse the structured knowledge and valuable information from related videos into the augmented graph node tokens. Finally, we construct an elaborate multi-video structured prompt to integrate the graph, visual, and textual tokens as the input to the large language model. Extensive experiments substantiate the effectiveness of our framework, showcasing its potential as a promising avenue for advancing video language models.

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title:
  Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning (early version)


author: Huang C, Yu F, Wan Z, Li F, Ji H, Li Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/39321562/
site: pubmed
title: Knowledge graph confidence-aware embedding for recommendation.
created_at: 2025-09-18 13:12:59.098227
summary:
  Knowledge graphs (KG) are vital for extracting and storing knowledge from large datasets. Current research favors knowledge graph-based recommendation methods, but they often overlook the features learning of relations between entities and focus excessively on entity-level details. Moreover, they ignore a crucial fact: the aggregation process of entity and relation features in KG is complex, diverse, and imbalanced. To address this, we propose a recommendation-oriented KG confidence-aware embedding technique. It introduces an information aggregation graph and a confidence feature aggregation mechanism to overcome these challenges. Additionally, we quantify entity confidence at the feature and category levels, improving the precision of embeddings during information propagation and aggregation. Our approach achieves significant improvements over state-of-the-art KG embedding-based recommendation methods, with up to 6.20% increase in AUC and 8.46% increase in GAUC, as demonstrated on four public KG datasets2.

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author: Mohamed SK, Nounu A, Nováček V.
original_link: https://pubmed.ncbi.nlm.nih.gov/32065227/
site: pubmed
title: Biological applications of knowledge graph embedding models.
created_at: 2025-09-18 13:12:59.098672
summary:
  Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph's inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug-target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.

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author: 陈可嘉;王朕卿;周修考
date: 2025-07-21 14:40
keywords: 文本分类;;BERT;;空间减缩注意力;;PVT;
source: 计算机工程与应用
site: 知网
title: 基于BERT-sPTT的文本分类模型
created_at: 2025-09-18 14:11:58.691009
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTub67m-SclQDCnKR58oGPVxdGVMSsAkPW049wHzGiZPe2MjkZRFBtEk73GFjc23CcODiCBXT3q1ZeXIE9uHHUUyFYnCKbtZ-61LG_4J7i2n1en-gMkfGqPd-EfMSfRhoYf5B1gdkc_6eNaVodiiab5fld768zmsigrxWOS3xAg8w==&uniplatform=NZKPT&language=CHS

summary:
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author: 杨泽斌;洪玲儿;胡亦菲;吴先球
date: 2025-06-20
keywords: 中文文本分类;;SOLO分类理论;;物理实验;;教学评价;
source: 物理实验
site: 知网
title: 中文文本分类在物理实验教学评价中的应用——以物理实验课后练习题文字回答的自动评分为例
created_at: 2025-09-18 14:11:58.693968
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTub67m-SclQDCnKR58oGPVti5yml5z4eeZZ3QQr6vDEMeD03RrgClNiTmp1rybe_6PxTwJrb7wDHxXm94YFqjF0mgSPbqVCbalnY0Lx5_dkV0rkPf4lVz3tABAfwkzOoXYRyUUCxGaQsAuTA8G1hUfr2IElEN-v7IpU34NuxrdFg==&uniplatform=NZKPT&language=CHS

summary:
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author: 于清
date: 2025-06-11
keywords: 文本情感分类;;文本多分类;;动态词向量;;深度学习;;注意力机制;
source: 烟台大学
site: 知网
title: 基于动态语义建模和特征提取的文本分类方法研究
created_at: 2025-09-18 14:11:58.695525
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTub67m-SclQDCnKR58oGPV0Z4JzcdGNb_1LnwjEZtvb4_w7uBmhD8eu_BvU-oLX5dFv42Fsn3u3RKqOdpoMLzDiTdilycXPDRrE84ZDTImk_7RDvYexsbR1uOkhuLFEWJYba_ZHLP8c7SeRQMzvAXyXgOlD2Am-6kXaY9VP57UWg==&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u793e\u4ea4\u5a92\u4f53\u4ea7\u751f\u5927\u91cf\u591a\u7c7b\u522b\u4e0e\u7ec6\u7c92\u5ea6\u60c5\u611f\u7684\u6587\u672c\uff0c\u4f20\u7edf\u5206\u7c7b\u65b9\u6cd5\u96be\u4ee5\u7cbe\u51c6\u8bc6\u522b\u590d\u6742\u8bed\u4e49\u4e0e\u7ec6\u81f4\u60c5\u611f\u8868\u8fbe\uff0c\u56e0\u6b64\u4e9f\u9700\u66f4\u667a\u80fd\u7684\u6587\u672c\u5206\u7c7b\u6280\u672f\u3002", "contribution": "1. \u9488\u5bf9\u4e0a\u4e0b\u6587\u8bed\u4e49\u7f3a\u5931\u6311\u6218\uff0c\u63d0\u51fa\u57fa\u4e8e\u52a8\u6001\u9884\u8bad\u7ec3\u4e0e\u6743\u91cd\u5206\u914d\u7684\u5c40\u90e8\u60c5\u611f\u7279\u5f81\u589e\u5f3a\u65b9\u6cd5\u3002\n2. \u521b\u65b0\u7ed3\u5408\u6df1\u5ea6\u5b66\u4e60\u4e0e\u6ce8\u610f\u529b\u673a\u5236\u4f18\u5316\u6587\u672c\u7c7b\u522b\u8bc6\u522b\uff0c\u63a8\u52a8\u7ec6\u7c92\u5ea6\u6587\u672c\u5206\u7c7b\u7814\u7a76\u8fdb\u5c55\u3002\n3. \u4e3a\u793e\u4ea4\u5a92\u4f53\u6587\u672c\u7684\u81ea\u52a8\u3001\u7cbe\u51c6\u5206\u7c7b\u63d0\u4f9b\u65b0\u65b9\u6848\uff0c\u5177\u6709\u73b0\u5b9e\u5e94\u7528\u4ef7\u503c\u3002", "method": "1. \u57fa\u4e8e\u52a8\u6001\u9884\u8bad\u7ec3\u8bed\u4e49\u8868\u793a\uff0c\u8bbe\u8ba1\u6743\u91cd\u5206\u914d\u7b56\u7565\u589e\u5f3a\u5c40\u90e8\u60c5\u611f\u7279\u5f81\uff0c\u5e76\u7528\u4e8e\u53e5\u5b50\u7ea7\u60c5\u611f\u5206\u7c7b\u3002\n2. \u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u53ca\u6ce8\u610f\u529b\u673a\u5236\uff0c\u5b9e\u73b0\u5bf9\u6587\u672c\u591a\u7c7b\u522b\u7684\u7cbe\u51c6\u7279\u5f81\u63d0\u53d6\u548c\u5206\u7c7b\u3002\n3. \u63d0\u51fa\u4e09\u79cd\u7ed3\u5408\u52a8\u6001\u8bed\u4e49\u5efa\u6a21\u4e0e\u7279\u5f81\u63d0\u53d6\u7684\u521b\u65b0\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u7528\u4e8e\u4e0d\u540c\u5206\u7c7b\u4efb\u52a1\u3002", "objective": "1. \u63d0\u5347\u53e5\u5b50\u7ea7\u60c5\u611f\u5206\u7c7b\u51c6\u786e\u6027\uff0c\u89e3\u51b3\u4e0a\u4e0b\u6587\u8bed\u4e49\u7f3a\u5931\u95ee\u9898\u3002\n2. \u63d0\u9ad8\u6587\u672c\u4e3b\u9898\u591a\u5206\u7c7b\u7684\u6548\u679c\uff0c\u9002\u5e94\u7c7b\u522b\u591a\u6837\u6027\u4e0e\u8868\u8fbe\u590d\u6742\u5316\u3002", "results": "\u63d0\u51fa\u7684\u65b9\u6cd5\u5728\u53e5\u5b50\u7ea7\u60c5\u611f\u5206\u7c7b\u548c\u591a\u7c7b\u522b\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e0a\u5747\u53d6\u5f97\u6bd4\u4f20\u7edf\u65b9\u6cd5\u66f4\u4f18\u7684\u8868\u73b0\uff0c\u63d0\u5347\u4e86\u5bf9\u590d\u6742\u60c5\u611f\u548c\u591a\u6837\u6587\u672c\u7c7b\u522b\u7684\u8bc6\u522b\u80fd\u529b\u3002"}


author: 黄锦
date: 2025-06-07
keywords: 文本分类;;图卷积网络;;多任务学习;;SBERT;
source: 大连交通大学
site: 知网
title: 基于图卷积网络的文本分类方法研究
created_at: 2025-09-18 14:11:58.696075
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTz9Wv4PhT8smfPiftoDBf4ZlHsZUzZnS_LyrrRXIfwPSaKsorjXRFGwlll6VKj4O-PmVKtsqzR9JhBydHBV76NYwnh-0OAm6Qf716qnoOW3ockjJw7UbdeM6ohC8i6OXwNII87XpY3Drlpn8akrYXsn4BSBoZDIWqgIzxkR98KzFW37SpzjiJb&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u6587\u672c\u5206\u7c7b\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7684\u5173\u952e\u4efb\u52a1\uff0c\u4f20\u7edf\u7684\u56fe\u5377\u79ef\u7f51\u7edc\uff08GCN\uff09\u65b9\u6cd5\u4ec5\u4f9d\u636e\u8bcd-\u6587\u6863\u56fe\u4e2d\u7684\u6587\u6863\u8282\u70b9\u8fdb\u884c\u5206\u7c7b\uff0c\u672a\u5145\u5206\u6316\u6398\u8bcd\u8282\u70b9\u7684\u534f\u52a9\u6f5c\u529b\u548c\u6df1\u5c42\u8bed\u4e49\u7279\u5f81\uff0c\u5f71\u54cd\u6a21\u578b\u8868\u73b0\u3002", "contribution": "1. \u63d0\u51fa\u57fa\u4e8eMish\u6fc0\u6d3b\u51fd\u6570\u4f18\u5316\u7684GCN\u6a21\u578b\uff0c\u663e\u8457\u6539\u5584\u6df1\u5c42\u8bed\u4e49\u4fe1\u606f\u4f20\u9012\u3002\n2. \u521b\u65b0\u6027\u5730\u878d\u5408\u591a\u4efb\u52a1\u5b66\u4e60\u673a\u5236\uff0c\u5c06\u8bcd\u6027\u53ca\u5b9e\u4f53\u8bc6\u522b\u5e2e\u52a9\u6587\u672c\u5206\u7c7b\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u548c\u8868\u8fbe\u80fd\u529b\u3002\n3. \u6539\u5584\u8282\u70b9\u7279\u5f81\u8868\u8fbe\uff0c\u589e\u5f3a\u4e86GCN\u5bf9\u6587\u672c\u590d\u6742\u7ed3\u6784\u7684\u5efa\u6a21\u80fd\u529b\uff0c\u4e3a\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u63d0\u4f9b\u4e86\u65b0\u7684\u601d\u8def\u3002", "method": "1. \u57fa\u4e8eText GCN\u6846\u67b6\uff0c\u5f15\u5165Mish\u6fc0\u6d3b\u51fd\u6570\u4ee5\u7f13\u89e3\u68af\u5ea6\u6d88\u5931\u3001\u4fc3\u8fdb\u4fe1\u606f\u4f20\u9012\u3002\n2. \u8bbe\u8ba1\u591a\u4efb\u52a1\u5b66\u4e60\uff0c\u5c06\u8bcd\u6027\u6807\u6ce8\u548c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u7eb3\u5165\u8f85\u52a9\u4efb\u52a1\uff0c\u4e30\u5bcc\u6a21\u578b\u8bed\u4e49\u8868\u8fbe\u3002\n3. \u6539\u8fdb\u8282\u70b9\u521d\u59cb\u5316\u65b9\u6cd5\uff0c\u4fc3\u8fdb\u6df1\u5ea6\u8bed\u4e49\u7279\u5f81\u63d0\u53d6\u3002", "objective": "1. \u4f18\u5316\u4f20\u7edfGCN\u5728\u6587\u672c\u5206\u7c7b\u4e2d\u7684\u8bed\u4e49\u4fe1\u606f\u5229\u7528\uff0c\u63d0\u5347\u5206\u7c7b\u51c6\u786e\u7387\u3002\n2. \u63a2\u7d22\u901a\u8fc7\u589e\u5f3a\u6fc0\u6d3b\u51fd\u6570\u548c\u591a\u4efb\u52a1\u673a\u5236\u63d0\u5347GCN\u6a21\u578b\u7684\u8bad\u7ec3\u4e0e\u8868\u8fbe\u80fd\u529b\u3002", "results": "\u6240\u63d0\u6539\u8fdb\u6a21\u578b\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e0a\u5b9e\u73b0\u4e86\u66f4\u9ad8\u7684\u51c6\u786e\u7387\uff0c\u6709\u6548\u63d0\u5347\u4e86\u8bed\u4e49\u8868\u8fbe\u80fd\u529b\u548c\u8bad\u7ec3\u6548\u679c\u3002"}


author: 邢义昀
date: 2025-06-06
keywords: 新闻文本分类;;特征融合;;层次标签;;预训练模型;
source: 齐鲁工业大学
site: 知网
title: 基于多元特征融合的新闻文本分类方法研究
created_at: 2025-09-18 14:11:58.696539
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTz9Wv4PhT8smfPiftoDBf4ZlHsZUzZnS_LyrrRXIfwPfh-xAU6zxPM4R31GuOvg3JyWa-SFKUIi6FO3HUDqCmQiGVOSFLSmmJbXSdnVOyk4Ws_2ybBJHRhfO8Q22N93103xs6tBAzfHTzJyhFFY9YRJCNckfxQuf_sPEJ7tzYx3g==&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u968f\u7740\u4e92\u8054\u7f51\u65b0\u95fb\u6570\u636e\u53ca\u4f20\u64ad\u6e20\u9053\u7684\u5feb\u901f\u589e\u957f\uff0c\u63d0\u5347\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u5df2\u6210\u4e3a\u4fe1\u606f\u7ba1\u7406\u3001\u63a8\u8350\u4e0e\u8206\u60c5\u9886\u57df\u7684\u5173\u952e\u9700\u6c42\u3002\u73b0\u6709\u65b9\u6cd5\u5728\u5904\u7406\u4e0a\u4e0b\u6587\u8bed\u4e49\u3001\u7279\u5f81\u8868\u8fbe\u53ca\u5c42\u6b21\u6807\u7b7e\u5229\u7528\u65b9\u9762\u5b58\u5728\u4e0d\u8db3\u3002", "contribution": "1. \u5728\u5355\u6807\u7b7e\u5206\u7c7b\u4e2d\uff0c\u63d0\u51fa\u7279\u5f81\u591a\u5143\u878d\u5408\u673a\u5236\uff0c\u663e\u8457\u589e\u5f3a\u8bed\u4e49\u8868\u8fbe\u4e0e\u7279\u5f81\u62bd\u53d6\u3002\n2. \u5728\u5c42\u6b21\u6807\u7b7e\u5206\u7c7b\u4e2d\uff0c\u521b\u65b0\u6027\u878d\u5165\u6807\u7b7e\u8def\u5f84\u53ca\u52a8\u6001\u878d\u5408\u7b56\u7565\uff0c\u63d0\u5347\u6807\u7b7e\u7ed3\u6784\u5229\u7528\u4e0e\u5206\u7c7b\u7cbe\u5ea6\u3002\n3. \u4e3a\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u5728\u5b9e\u9645\u63a8\u8350\u4e0e\u8206\u60c5\u7cfb\u7edf\u5e94\u7528\u4e2d\u63d0\u4f9b\u4e86\u66f4\u9ad8\u6548\u3001\u53ef\u6269\u5c55\u7684\u6a21\u578b\u9009\u62e9\u3002", "method": "1. \u9488\u5bf9\u5355\u6807\u7b7e\u5206\u7c7b\uff0c\u63d0\u51faCBDN\u6a21\u578b\uff1a\u91c7\u7528\u5168\u8bcd\u63a9\u7801BERT-wwm\u4f18\u5316\u5d4c\u5165\uff1b\u7ed3\u5408\u53cc\u5411LSTM\u4e0e\u591a\u5934\u6ce8\u610f\u529b\u673a\u5236\u6355\u6349\u65f6\u5e8f\u4e0e\u591a\u89c6\u89d2\u8bed\u4e49\uff1b\u4ee5\u6539\u8fdb\u91d1\u5b57\u5854\u5377\u79ef\u7f51\u7edc\u4e0e\u95e8\u63a7\u673a\u5236\u878d\u5408\u591a\u5c3a\u5ea6\u3001\u5c40\u90e8\u548c\u5168\u5c40\u7279\u5f81\u3002\n2. \u9488\u5bf9\u5c42\u6b21\u5206\u7c7b\uff0c\u63d0\u51faDHFPE\u6a21\u578b\uff1a\u4f9d\u6258RoBERTa\u53ca\u63d0\u793a\u5d4c\u5165\u673a\u5236\uff0c\u5c06\u5c42\u6b21\u6807\u7b7e\u8def\u5f84\u878d\u5165\u8f93\u5165\uff1b\u52a8\u6001\u878d\u5408\u5c42\u6b21\u7279\u5f81\uff1b\u7528\u6ce8\u610f\u529b\u5bf9\u6bd4\u5b66\u4e60\u7b56\u7565\u589e\u5f3a\u7279\u5f81\u533a\u5206\u5ea6\u3002", "objective": "1. \u63d0\u9ad8\u65b0\u95fb\u6587\u672c\u5355\u6807\u7b7e\u5206\u7c7b\u7684\u4e0a\u4e0b\u6587\u8bed\u4e49\u4e0e\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u3002\n2. \u4f18\u5316\u5c42\u6b21\u6807\u7b7e\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u4e2d\u7684\u6807\u7b7e\u5c42\u6b21\u4fe1\u606f\u5229\u7528\u4e0e\u7ed3\u6784\u4ea4\u4e92\u6548\u7387\u3002", "results": "\u7cfb\u7edf\u5b9e\u9a8c\u663e\u793a\uff0c\u4e24\u79cd\u878d\u5408\u65b9\u6cd5\u5728\u5404\u81ea\u4efb\u52a1\u573a\u666f\u4e0b\u63d0\u5347\u4e86\u6587\u672c\u8868\u793a\u8d28\u91cf\u548c\u5206\u7c7b\u6027\u80fd\uff0c\u4f18\u4e8e\u4f20\u7edf\u65b9\u6cd5\u3002"}


author: 顾启梁
date: 2025-06-06
keywords: 多标签文本分类;;司法文本;;标签语义嵌入;;特征融合;;深度学习;
source: 齐鲁工业大学
site: 知网
title: 面向司法文本的多标签文本分类方法研究
created_at: 2025-09-18 14:11:58.697027
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DTz9Wv4PhT8smfPiftoDBf4ZlHsZUzZnS_LyrrRXIfwPbsyTdRsFDAhBRMfdl0UjAs-wYqt9X4XG6UvWW6G_oo5dnSfmWgHBv1q1KNSyauxViyNTt7mwtbLxdNsJcxfuFzWL529WpVXgEer2H7BlaDx7mPgaRKjnxO0dV_V02Karg==&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u6570\u5b57\u5316\u8fdb\u6b65\u63a8\u52a8\u53f8\u6cd5\u9886\u57df\u6cd5\u5f8b\u6587\u6863\u6570\u91cf\u6fc0\u589e\uff0c\u51c6\u786e\u9ad8\u6548\u5730\u5bf9\u8fd9\u4e9b\u590d\u6742\u6587\u672c\u8fdb\u884c\u591a\u6807\u7b7e\u5206\u7c7b\u6210\u4e3a\u6cd5\u5f8b\u4fe1\u606f\u68c0\u7d22\u548c\u6848\u4ef6\u5904\u7406\u7684\u91cd\u8981\u9700\u6c42\u3002\u7136\u800c\u73b0\u6709\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\u9762\u4e34\u7279\u5f81\u5229\u7528\u4e0d\u8db3\u3001\u6807\u7b7e\u548c\u6587\u672c\u5173\u8054\u5ea6\u5efa\u6a21\u8f83\u5f31\u3001\u5faa\u73af\u6a21\u578b\u4fe1\u606f\u635f\u5931\u4e0e\u6807\u7b7e\u4f9d\u8d56\u5173\u7cfb\u5efa\u6a21\u4e0d\u8db3\u7b49\u96be\u9898\u3002", "contribution": "1. \u9996\u6b21\u7cfb\u7edf\u5206\u6790\u5e76\u89e3\u51b3\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u4e2d\u7684\u7279\u5f81\u878d\u5408\u3001\u6807\u7b7e\u5d4c\u5165\u548c\u6807\u7b7e\u76f8\u5173\u6027\u7b49\u6838\u5fc3\u96be\u9898\u3002\n2. \u63d0\u51fa\u5e76\u9a8c\u8bc1\u591a\u7279\u5f81\u878d\u5408\u548c\u6807\u7b7e\u5d4c\u5165\u65b0\u65b9\u6cd5\uff0c\u63a8\u52a8\u4e86\u53f8\u6cd5\u9886\u57df\u6587\u672c\u667a\u80fd\u5904\u7406\u6280\u672f\u53d1\u5c55\u3002\n3. \u663e\u8457\u63d0\u5347\u4e86\u53f8\u6cd5\u6587\u6863\u81ea\u52a8\u5206\u7c7b\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\uff0c\u4e3a\u6cd5\u5f8b\u4fe1\u606f\u68c0\u7d22\u548c\u8f85\u52a9\u51b3\u7b56\u63d0\u4f9b\u4e86\u66f4\u6709\u529b\u7684\u5de5\u5177\u3002", "method": "1. \u63d0\u51fa\u7ed3\u5408\u591a\u7279\u5f81\u878d\u5408\u4e0e\u6807\u7b7e\u5d4c\u5165\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u4ece\u4e0d\u540c\u5c42\u6b21\u63d0\u53d6\u5e76\u878d\u5408\u53f8\u6cd5\u6587\u672c\u7279\u5f81\u3002\n2. \u6784\u5efa\u6807\u7b7e\u5d4c\u5165\u673a\u5236\uff0c\u589e\u5f3a\u6807\u7b7e\u4e0e\u6587\u672c\u4e4b\u95f4\u7684\u8bed\u4e49\u5173\u8054\u3002\n3. \u9488\u5bf9\u6807\u7b7e\u76f8\u5173\u6027\u5efa\u6a21\u4e0e\u6ce8\u610f\u529b\u673a\u5236\u8bbe\u8ba1\uff0c\u589e\u5f3a\u5c40\u90e8\u4e0e\u5168\u5c40\u4fe1\u606f\u7684\u878d\u5408\u80fd\u529b\uff0c\u51cf\u5c11\u5faa\u73af\u7f51\u7edc\u4fe1\u606f\u635f\u5931\u3002", "objective": "1. \u89e3\u51b3\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u4e2d\u7684\u7279\u5f81\u63d0\u53d6\u53ca\u5229\u7528\u4e0d\u8db3\u95ee\u9898\u3002\n2. \u52a0\u5f3a\u6807\u7b7e\u4e0e\u6587\u672c\u4e4b\u95f4\u7684\u8bed\u4e49\u5173\u7cfb\u5efa\u6a21\u3002\n3. \u6539\u5584\u6807\u7b7e\u4e4b\u95f4\u591a\u91cd\u76f8\u5173\u6027\u6355\u6349\uff0c\u63d0\u5347\u5206\u7c7b\u51c6\u786e\u6027\u4e0e\u5b9e\u7528\u6027\u3002", "results": "\u6240\u63d0\u6a21\u578b\u5728\u7f6a\u540d\u3001\u6cd5\u5f8b\u6761\u6587\u548c\u5211\u671f\u7b49\u53f8\u6cd5\u591a\u6807\u7b7e\u5206\u7c7b\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u4f18\u4e8e\u4e3b\u6d41\u65b9\u6cd5\u7684\u8868\u73b0\uff0c\u6709\u6548\u63d0\u5347\u4e86\u5206\u7c7b\u51c6\u786e\u7387\u548c\u6807\u7b7e\u5224\u5b9a\u7684\u4e00\u81f4\u6027\u3002"}


author: 曹文宇
date: 2025-06-05
keywords: 海量文本分类;;图卷积神经网络;;BERT;;多层次注意力机制;;特征融合;
source: 内蒙古科技大学
site: 知网
title: 基于深度学习的海量文本分类技术研究
created_at: 2025-09-18 14:11:58.697494
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DR0PJTcTN9WCUPykrlonrg6E5zYPSEayN0DsobmkMH740AA7WF7wSzKRqdSTQZPQudinspTtGtyZWo5aBp6J-un0YtH8AnE3MBi39DsJ5h9Fb1N6iq8LN2FO0KRNk9k4kRI5weNX6_Pk0-hVhk7zccEiL0M6d2oz292jgMzW-DGVg==&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u968f\u7740\u4e92\u8054\u7f51\u548c\u793e\u4ea4\u5a92\u4f53\u6570\u636e\u91cf\u66b4\u589e\uff0c\u4f20\u7edf\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u96be\u4ee5\u5e94\u5bf9\u9ad8\u7ef4\u3001\u52a8\u6001\u3001\u975e\u7ed3\u6784\u5316\u6587\u672c\u6570\u636e\u7684\u9ad8\u6548\u5206\u7c7b\u9700\u6c42\uff0c\u6df1\u5ea6\u5b66\u4e60\u5c24\u5176\u662f\u56fe\u795e\u7ecf\u7f51\u7edc\u548c\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u4e3a\u6b64\u5e26\u6765\u65b0\u673a\u9047\uff0c\u4f46\u5f53\u524d\u6280\u672f\u5b58\u5728\u5bf9\u5168\u5c40\u7ed3\u6784\u4e0e\u4e0a\u4e0b\u6587\u8bed\u4e49\u878d\u5408\u4e0d\u8db3\u7b49\u9650\u5236\u3002", "contribution": "1. \u4e30\u5bcc\u4e86\u57fa\u4e8e\u56fe\u795e\u7ecf\u7f51\u7edc\u548c\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u6587\u672c\u5206\u7c7b\u7684\u6280\u672f\u4f53\u7cfb\uff0c\u63a8\u52a8\u5176\u7ed3\u5408\u4e0e\u53d1\u5c55\u30022. \u63d0\u51fa\u52a8\u6001\u8fb9\u6743\u91cd\u548c\u591a\u5c42\u6b21\u7279\u5f81\u878d\u5408\u7b56\u7565\uff0c\u6709\u6548\u5f25\u8865\u4e86\u73b0\u6709\u6df1\u5ea6\u6a21\u578b\u5bf9\u7ed3\u6784\u4fe1\u606f\u4e0e\u8bed\u4e49\u4fe1\u606f\u5efa\u6a21\u7684\u4e0d\u8db3\u3002", "method": "1. \u5bf9\u4e8e\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u63d0\u51fa\u7ed3\u5408\u53d8\u5206\u7ed3\u6784\u548c\u52a8\u6001\u8fb9\u6743\u91cd\u5b66\u4e60\u7684\u6539\u8fdb\u6a21\u578b\uff0c\u5b9e\u73b0\u7075\u6d3b\u9002\u5e94\u6587\u672c\u6837\u672c\u7279\u5f81\u5dee\u5f02\u30022. \u5bf9\u4e8eBERT\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u91c7\u7528\u57fa\u4e8e\u591a\u5c42\u6b21\u6ce8\u610f\u529b\u673a\u5236\u7684\u7279\u5f81\u878d\u5408\u65b9\u6cd5\uff0c\u589e\u5f3a\u6a21\u578b\u5bf9\u5168\u5c40\u62d3\u6251\u7ed3\u6784\u548c\u4e0a\u4e0b\u6587\u8bed\u4e49\u7684\u7efc\u5408\u5efa\u6a21\u80fd\u529b\u3002", "objective": "1. \u63d0\u5347\u9762\u5bf9\u5927\u89c4\u6a21\u3001\u590d\u6742\u6587\u672c\u6570\u636e\u65f6\u7684\u5206\u7c7b\u51c6\u786e\u7387\u4e0e\u6cdb\u5316\u80fd\u529b\u30022. \u9488\u5bf9\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4e0eBERT\u6a21\u578b\u5404\u81ea\u7684\u5c40\u9650\u6027\u63d0\u51fa\u6709\u6548\u6539\u8fdb\u7b56\u7565\u3002", "results": "\u4e24\u9879\u6539\u8fdb\u65b9\u6cd5\u5747\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u663e\u8457\u63d0\u5347\u4e86\u5206\u7c7b\u6027\u80fd\uff0c\u517c\u5177\u66f4\u9ad8\u7684\u51c6\u786e\u6027\u548c\u66f4\u5f3a\u7684\u6cdb\u5316\u80fd\u529b\u3002"}


author: 李胜南
date: 2025-06-01
keywords: 医疗文本;;异构图卷积网络;;图扩散;;文本异构图;;文本分类;
source: 长春工业大学
site: 知网
title: 基于异构图扩散网络的医疗文本分类方法研究
created_at: 2025-09-18 14:11:58.697965
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DR0PJTcTN9WCUPykrlonrg6E5zYPSEayN0DsobmkMH74zfaY4tPJqgFsVfqkolJwnFwQURFcm6-xx1Fqww77PKwPSUbHV7TEqa2TnzjYgYWowhOeuDf0TG3vgFl8i4VsaWqNkO9bY-h6-9angX2yWzlrJGyS0RCimqYizyNYvaz2w==&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u533b\u7597\u6587\u672c\u5206\u7c7b\u6709\u52a9\u4e8e\u63d0\u5347\u533b\u7597\u4fe1\u606f\u68c0\u7d22\u548c\u667a\u80fd\u5316\u6c34\u5e73\uff0c\u4f46\u56e0\u4e13\u4e1a\u672f\u8bed\u591a\u3001\u7ed3\u6784\u590d\u6742\uff0c\u5bf9\u5206\u7c7b\u65b9\u6cd5\u63d0\u51fa\u4e86\u9ad8\u51c6\u786e\u6027\u548c\u7ed3\u6784\u5316\u5efa\u6a21\u7684\u6311\u6218\u3002\u73b0\u6709\u4e3b\u6d41\u65b9\u6cd5\u5f80\u5f80\u672a\u80fd\u5145\u5206\u5229\u7528\u6587\u672c\u5185\u90e8\u7684\u7ed3\u6784\u5173\u7cfb\u53ca\u957f\u7a0b\u4f9d\u8d56\uff0c\u5bfc\u81f4\u5206\u7c7b\u7cbe\u5ea6\u53d7\u9650\u3002", "contribution": "1. \u9996\u6b21\u5728\u533b\u7597\u6587\u672c\u5206\u7c7b\u4e2d\u7cfb\u7edf\u6784\u5efa\u5e76\u5e94\u7528\u6587\u672c\u5f02\u6784\u56fe\uff0c\u5f3a\u5316\u4e86\u7ed3\u6784\u5efa\u6a21\u80fd\u529b\u3002\n2. \u521b\u65b0\u6027\u5730\u5f15\u5165\u56fe\u6269\u6563\u673a\u5236\u4ee5\u6355\u6349\u957f\u7a0b\u4f9d\u8d56\uff0c\u8865\u8db3\u4ee5\u5f80\u65b9\u6cd5\u4e0d\u8db3\u3002\n3. \u63d0\u4f9b\u4e86\u4e00\u5957\u901a\u7528\u7684\u7ed3\u6784\u4fe1\u606f\u5efa\u6a21\u4e0e\u5206\u7c7b\u65b9\u6cd5\uff0c\u4e3a\u667a\u6167\u533b\u7597\u8fdb\u4e00\u6b65\u667a\u80fd\u5316\u53d1\u5c55\u5960\u5b9a\u57fa\u7840\u3002", "method": "1. \u8bbe\u8ba1\u5e76\u6784\u5efa\u6587\u672c\u5f02\u6784\u56fe\uff0c\u878d\u5408\u5355\u8bcd-\u5355\u8bcd\u3001\u53e5\u5b50-\u53e5\u5b50\u548c\u5355\u8bcd-\u53e5\u5b50\u7684\u591a\u5c42\u6b21\u5173\u7cfb\uff0c\u6355\u6349\u6587\u672c\u5185\u90e8\u7ed3\u6784\u548c\u5168\u5c40\u4fe1\u606f\u3002\n2. \u5e94\u7528\u56fe\u6269\u6563\u65b9\u6cd5\u5efa\u6a21\u8fdc\u7a0b\u4f9d\u8d56\uff0c\u589e\u5f3a\u957f\u8ddd\u79bb\u5173\u7cfb\u63d0\u53d6\u80fd\u529b\u3002\n3. \u7ed3\u5408\u5f02\u6784\u56fe\u5377\u79ef\u7f51\u7edc\u4e0e\u591a\u5c42\u611f\u77e5\u5668\uff0c\u5b9e\u73b0\u5bf9\u533b\u7597\u6587\u672c\u7684\u81ea\u52a8\u5206\u7c7b\u3002\n4. \u901a\u8fc7\u6d88\u878d\u5b9e\u9a8c\u9a8c\u8bc1\u5404\u7ec4\u4ef6\u5bf9\u6027\u80fd\u7684\u8d21\u732e\u3002", "objective": "1. \u89e3\u51b3\u533b\u7597\u6587\u672c\u5206\u7c7b\u4e2d\u7ed3\u6784\u4fe1\u606f\u5229\u7528\u4e0d\u8db3\u548c\u96be\u4ee5\u5efa\u6a21\u957f\u7a0b\u4f9d\u8d56\u7684\u95ee\u9898\u3002\n2. \u63d0\u5347\u533b\u7597\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u5bf9\u4e13\u4e1a\u7ed3\u6784\u7684\u523b\u753b\u80fd\u529b\u3002", "results": "\u6240\u63d0\u5f02\u6784\u56fe\u6269\u6563\u7f51\u7edc\u65b9\u6cd5\u663e\u8457\u63d0\u5347\u4e86\u533b\u7597\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\uff0c\u5c24\u5176\u5728\u6355\u6349\u7ed3\u6784\u5316\u4fe1\u606f\u548c\u8fdc\u7a0b\u4f9d\u8d56\u65b9\u9762\u8868\u73b0\u4f18\u8d8a\uff0c\u6838\u5fc3\u7ec4\u4ef6\u7684\u6709\u6548\u6027\u5f97\u5230\u5b9e\u9a8c\u9a8c\u8bc1\u3002"}


author: 刘克爽
date: 2025-06-01
keywords: 文本分类;;图卷积神经网络;;癌症文本;;多视图;
source: 长春工业大学
site: 知网
title: 融合多视图和图卷积神经网络的癌症文本分类方法研究
created_at: 2025-09-18 14:11:58.698415
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DR0PJTcTN9WCUPykrlonrg6E5zYPSEayN0DsobmkMH74-Fu_CE6PtShIxPpzNb3bOHHiR7K8CGWIXT7Z1xGSppPA0EqOvui4IYche0jd_l7752IBNchQ_48z1YPkE9nyLTqj9cOdOaU3u5IAdLAsM27t22EXAtXdm4OUJTGPJ-naA==&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u533b\u7597\u4fe1\u606f\u5316\u8fdb\u7a0b\u52a0\u901f\uff0c\u4f7f\u764c\u75c7\u76f8\u5173\u6587\u672c\u6570\u636e\u6fc0\u589e\uff0c\u56e0\u5176\u4e13\u4e1a\u6027\u548c\u7ed3\u6784\u7279\u6b8a\u6027\uff0c\u4f20\u7edf\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u96be\u4ee5\u6709\u6548\u5904\u7406\u8fd9\u4e9b\u533b\u5b66\u6587\u672c\u3002", "contribution": "1. \u9996\u6b21\u5c06\u591a\u89c6\u56fe\u4e0e\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7ed3\u5408\u4e8e\u764c\u75c7\u6587\u672c\u5206\u7c7b\u573a\u666f\uff0c\u663e\u8457\u63d0\u5347\u5206\u7c7b\u6027\u80fd\u30022. \u9488\u5bf9\u4e13\u4e1a\u672f\u8bed\u53ca\u590d\u6742\u6587\u672c\u7ed3\u6784\uff0c\u63d0\u51fa\u8282\u70b9\u7279\u5f81\u4e0e\u591a\u89c6\u56fe\u878d\u5408\u7684\u65b0\u89e3\u51b3\u601d\u8def\u30023. \u4e3a\u533b\u5b66\u6587\u672c\u667a\u80fd\u5904\u7406\u548c\u540e\u7eed\u76f8\u5173\u5e94\u7528\u7814\u53d1\u63d0\u4f9b\u6709\u529b\u6280\u672f\u53c2\u8003\u3002", "method": "1. \u5229\u7528\u6587\u672c\u8868\u793a\u5b66\u4e60\uff0c\u5206\u522b\u6784\u5efa\u5355\u8bcd\u8282\u70b9\u548c\u53e5\u5b50\u8282\u70b9\u7279\u5f81\uff0c\u5e76\u5f62\u6210\u4e24\u79cd\u89c6\u56fe\u30022. \u9488\u5bf9\u4e0d\u540c\u89c6\u56fe\u5206\u522b\u8bbe\u8ba1\u4e13\u95e8\u7684\u56fe\u5377\u79ef\u6a21\u5757\uff0c\u5b66\u4e60\u8282\u70b9\u5c42\u7ea7\u7279\u5f81\u30023. \u5c06\u4e24\u89c6\u56fe\u7279\u5f81\u878d\u5408\uff0c\u5f97\u51fa\u6587\u672c\u5168\u5c40\u8868\u793a\uff0c\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1\u30024. \u5728\u764c\u75c7\u6587\u672c\u6570\u636e\u4e0a\u8fdb\u884c\u5b9e\u8bc1\u6bd4\u8f83\uff0c\u4e0e\u73b0\u6709\u65b9\u6cd5\u8bc4\u4f30\u5206\u7c7b\u6548\u679c\u3002", "objective": "1. \u5206\u6790\u764c\u75c7\u6587\u672c\u5206\u7c7b\u6280\u672f\u5728\u533b\u5b66\u9886\u57df\u7684\u5e94\u7528\u73b0\u72b6\u53ca\u5173\u952e\u6311\u6218\u30022. \u63d0\u51fa\u5e76\u9a8c\u8bc1\u4e00\u79cd\u878d\u5408\u591a\u89c6\u56fe\u548c\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u65b0\u65b9\u6cd5\uff0c\u63d0\u5347\u764c\u75c7\u6587\u672c\u5206\u7c7b\u6027\u80fd\u3002", "results": "\u591a\u89c6\u56fe\u878d\u5408\u56fe\u5377\u79ef\u65b9\u6cd5\u5728\u764c\u75c7\u6587\u672c\u5206\u7c7b\u51c6\u786e\u6027\u4e0a\u663e\u8457\u4f18\u4e8e\u4ee5\u5f80\u6a21\u578b\uff0c\u6709\u6548\u5904\u7406\u4e86\u6587\u672c\u4e2d\u7684\u4e13\u4e1a\u672f\u8bed\u548c\u7ed3\u6784\u590d\u6742\u6027\u3002"}


author: 刘琪
date: 2025-06-01
keywords: 文本分类;;预训练模型;;图神经网络;;特征融合;
source: 北京印刷学院
site: 知网
title: 基于预训练模型的图神经网络文本分类方法研究
created_at: 2025-09-18 14:11:58.698897
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=4QAaWarC2DToObLuQfknTlAHmROyJ5NXZWoFZY5WH0Jr1eV_uUtvn5Av4RxSYm8-mr60k2DvOvCD5quWjb25Vi03uKTQUTHM_xWMdACNZ9F5K0XVyG__AHhk1Kc_P0KYij6zKWCXnzkZRgWcV-YzzSN3B7VCes6VnODFlFsN4vqRbUuJ3Lv_3izGpNab6JVv&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 504 characters]

parsed_summary:
  {"background": "\u4f20\u7edf\u6587\u672c\u5206\u7c7b\u53d7\u9650\u4e8e\u96be\u4ee5\u5145\u5206\u5efa\u6a21\u6587\u672c\u95f4\u590d\u6742\u5173\u7cfb\u548c\u6df1\u5c42\u8bed\u4e49\u4fe1\u606f\u3002\u5c3d\u7ba1\u56fe\u795e\u7ecf\u7f51\u7edc\u548c\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u5206\u522b\u80fd\u6355\u6349\u7ed3\u6784\u4e0e\u8bed\u4e49\uff0c\u4f46\u5355\u72ec\u4f7f\u7528\u5b58\u5728\u5404\u81ea\u4e0d\u8db3\uff0c\u878d\u5408\u65b9\u6cd5\u5982BertGCN\u867d\u63d0\u5347\u4e86\u6027\u80fd\uff0c\u4f46\u4ecd\u53d7\u8fc1\u79fb\u5b66\u4e60\u8868\u5f81\u4e0d\u4e00\u81f4\u4e0e\u957f\u8ddd\u79bb\u4f9d\u8d56\u6355\u83b7\u80fd\u529b\u4e0d\u8db3\u7b49\u95ee\u9898\u56f0\u6270\u3002", "contribution": "1. \u63d0\u51fa\u57fa\u4e8e\u9690\u85cf\u7a7a\u95f4\u6df7\u5408\u589e\u5f3a\u7684\u6587\u672c\u5206\u7c7b\u65b0\u65b9\u6cd5\uff0c\u6709\u6548\u7f13\u89e3\u9884\u8bad\u7ec3\u4e0e\u4e0b\u6e38\u4efb\u52a1\u8868\u5f81\u4e0d\u4e00\u81f4\u7684\u95ee\u9898\u3002\n2. \u4f18\u5316\u4e86\u6587\u672c\u7ed3\u6784\u548c\u8bed\u4e49\u4fe1\u606f\u7684\u8054\u5408\u5efa\u6a21\u65b9\u5f0f\uff0c\u663e\u8457\u63d0\u5347\u4e86\u56fe\u795e\u7ecf\u7f51\u7edc\u6587\u672c\u5206\u7c7b\u7684\u6548\u679c\u3002\n3. \u4e3a\u878d\u5408\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u65b9\u6cd5\u63d0\u4f9b\u4e86\u65b0\u7684\u601d\u8def\u548c\u5b9e\u8bc1\u57fa\u7840\u3002", "method": "1. \u63d0\u51fa\u57fa\u4e8e\u9690\u85cf\u5c42\u7a7a\u95f4\u6df7\u5408\u589e\u5f3a\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u91c7\u7528XLNet\u66ff\u4ee3BERT\u4ee5\u51cf\u5c11\u9884\u8bad\u7ec3\u4e0e\u4e0b\u6e38\u4efb\u52a1\u76ee\u6807\u7684\u4e0d\u4e00\u81f4\u6027\u3002\n2. \u5229\u7528\u56fe\u795e\u7ecf\u7f51\u7edc\u6784\u5efa\u6587\u672c\u7ed3\u6784\uff0c\u7ed3\u5408\u6539\u8fdb\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u8fdb\u884c\u7279\u5f81\u878d\u5408\uff0c\u5b9e\u73b0\u5bf9\u6587\u672c\u8bed\u4e49\u548c\u7ed3\u6784\u4fe1\u606f\u7684\u8054\u5408\u5efa\u6a21\u3002\n3. \u5728\u591a\u4e2a\u6587\u672c\u5206\u7c7b\u6570\u636e\u96c6\u4e0a\u5bf9\u6bd4\u5b9e\u9a8c\uff0c\u8bc4\u4f30\u6240\u63d0\u65b9\u6cd5\u7684\u6709\u6548\u6027\u3002", "objective": "1. \u9488\u5bf9\u9884\u8bad\u7ec3\u6a21\u578b\u4e0e\u4e0b\u6e38\u4efb\u52a1\u5728\u6587\u672c\u7ed3\u6784\u8868\u5f81\u65b9\u9762\u7684\u4e0d\u4e00\u81f4\u6027\uff0c\u63d0\u5347\u6a21\u578b\u7684\u7ed3\u6784\u8868\u8fbe\u4e0e\u957f\u8ddd\u79bb\u4f9d\u8d56\u6355\u6349\u80fd\u529b\u3002\n2. \u63d0\u9ad8\u878d\u5408\u9884\u8bad\u7ec3\u6a21\u578b\u4e0e\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u6587\u672c\u5206\u7c7b\u6a21\u578b\u7684\u6574\u4f53\u6027\u80fd\uff0c\u7f13\u89e3\u5df2\u6709\u65b9\u6cd5\u5c40\u9650\u3002", "results": "\u63d0\u51fa\u7684\u65b9\u6cd5\u5728\u591a\u4e2a\u6587\u672c\u5206\u7c7b\u57fa\u51c6\u6570\u636e\u96c6\u4e0a\u53d6\u5f97\u4e86\u4f18\u4e8eBertGCN\u7b49\u5bf9\u6bd4\u65b9\u6cd5\u7684\u51c6\u786e\u7387\u548c\u7efc\u5408\u6027\u80fd\uff0c\u5c24\u5176\u5728\u957f\u8ddd\u79bb\u4f9d\u8d56\u5173\u7cfb\u5efa\u6a21\u65b9\u9762\u8868\u73b0\u7a81\u51fa\u3002"}


author: 宋东桓;胡懋地;丁洁兰
date: 2025年2期
keywords: 多任务学习;跨类型文本分类;迁移学习;集成学习
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 基于多任务学习的跨类型文本分类技术研究
created_at: 2025-09-18 14:11:58.699362
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAyGghkNml0bWZpOQ%3D%3D

summary:
[cell repeats an earlier one in this file: 312 characters]

parsed_summary:
  {"background": "\u4f20\u7edf\u6587\u672c\u5206\u7c7b\u56e0\u9886\u57df\u6570\u636e\u4e0d\u8db3\u548c\u7c7b\u578b\u95f4\u5dee\u5f02\u5927\uff0c\u5bfc\u81f4\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u5f31\uff0c\u5206\u7c7b\u51c6\u786e\u7387\u96be\u4ee5\u63d0\u5347\u3002", "contribution": "1. \u9996\u6b21\u5c06BERT\u3001DPCNN\u3001MMOE\u4e09\u8005\u7ed3\u5408\u5e94\u7528\u4e8e\u8de8\u7c7b\u578b\u6587\u672c\u5206\u7c7b\uff0c\u63d0\u5347\u6a21\u578b\u9002\u5e94\u6027\u4e0e\u6548\u679c\u30022. \u4e3a\u591a\u4efb\u52a1\u573a\u666f\u4e0b\u6587\u672c\u667a\u80fd\u5206\u7c7b\u63d0\u4f9b\u6709\u6548\u53ef\u884c\u7684\u6280\u672f\u8def\u5f84\u3002", "method": "1. \u6784\u5efa\u878d\u5408BERT\u3001\u6df1\u5c42\u91d1\u5b57\u5854\u5377\u79ef\u7f51\u7edc\uff08DPCNN\uff09\u4e0e\u591a\u95e8\u4e13\u5bb6\u63a7\u5236\u5355\u5143\uff08MMOE\uff09\u7684\u591a\u4efb\u52a1\u5b66\u4e60\u6a21\u578b\u30022. \u901a\u8fc7\u81ea\u4e3b\u6784\u5efa\u8de8\u7c7b\u578b\u591a\u4efb\u52a1\u6570\u636e\u96c6\uff0c\u8bbe\u8ba1\u591a\u4efb\u52a1\u4e0e\u8fc1\u79fb\u5b66\u4e60\u5b9e\u9a8c\uff0c\u5e76\u4e0e8\u79cd\u4e3b\u6d41\u57fa\u7ebf\u6a21\u578b\u8fdb\u884c\u5bf9\u6bd4\u3002", "objective": "1. \u63d0\u9ad8\u6587\u672c\u5206\u7c7b\u5728\u591a\u9886\u57df\u548c\u591a\u7c7b\u578b\u573a\u666f\u4e0b\u7684\u51c6\u786e\u7387\u4e0e\u6cdb\u5316\u80fd\u529b\u30022. \u9a8c\u8bc1\u591a\u4efb\u52a1\u673a\u5236\u4e0e\u6df1\u5c42\u795e\u7ecf\u7f51\u7edc\u7ed3\u5408\u5bf9\u8de8\u7c7b\u578b\u6587\u672c\u5206\u7c7b\u7684\u6709\u6548\u6027\u3002", "results": "\u63d0\u51fa\u6a21\u578b\u5728\u591a\u4efb\u52a1\u53ca\u8fc1\u79fb\u5b66\u4e60\u5b9e\u9a8c\u4e2d\uff0c\u5206\u7c7b\u6027\u80fd\u4f18\u4e8e\u6240\u6709\u57fa\u7ebf\u6a21\u578b\uff0cF1\u503c\u63d0\u5347\u5747\u8d854.7\u4e2a\u767e\u5206\u70b9\u3002"}


author: 于娟;赵慧云;巫邵诚
date: 2025年2期
keywords: 跨语言;文本分类;句向量加权;文本相似度
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 基于句向量加权的跨语言文本分类方法
created_at: 2025-09-18 14:11:58.699816
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA0GghkNml0bWZpOQ%3D%3D

summary:
[cell repeats an earlier one in this file: 327 characters]

parsed_summary:
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author: 张静;高子信;丁伟杰
date: 2025年2期
keywords: BERT;DPCNN;警情文本分类;激活函数
quote: 1
source: 《数据分析与知识发现》
site: 万方
title: 基于BERT-DPCNN的警情文本分类研究
created_at: 2025-09-18 14:11:58.700286
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA1GghkNml0bWZpOQ%3D%3D

summary:
[cell repeats an earlier one in this file: 353 characters]

parsed_summary:
  {"background": "\u968f\u7740\u8b66\u52a1\u4fe1\u606f\u5316\u53d1\u5c55\uff0c\u516c\u5b89\u90e8\u95e8\u79ef\u7d2f\u4e86\u5927\u91cf\u8b66\u60c5\u6587\u672c\u6570\u636e\uff0c\u5982\u4f55\u9ad8\u6548\u3001\u51c6\u786e\u5730\u5bf9\u8fd9\u4e9b\u6570\u636e\u8fdb\u884c\u5206\u7c7b\u5206\u6790\u6210\u4e3a\u4e9f\u9700\u89e3\u51b3\u7684\u95ee\u9898\u3002", "contribution": "1. \u5f00\u53d1\u4e86\u4e00\u79cd\u7ed3\u5408BERT\u548c\u6539\u8fdbDPCNN\u7684\u65b0\u578b\u8b66\u60c5\u6587\u672c\u5206\u7c7b\u6a21\u578b\uff0c\u6709\u6548\u63d0\u5347\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u5206\u7c7b\u51c6\u786e\u7387\u3002\n2. \u4e3a\u516c\u5b89\u5b9e\u6218\u8b66\u60c5\u5206\u6790\u4e0e\u7814\u5224\u63d0\u4f9b\u4e86\u66f4\u4e3a\u53ef\u9760\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u63a8\u52a8\u8b66\u60c5\u667a\u80fd\u5316\u5904\u7406\u53d1\u5c55\u3002", "method": "1. \u57fa\u4e8eBERT\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u751f\u6210\u8b66\u60c5\u6587\u672c\u8bcd\u5411\u91cf\uff0c\u63d0\u5347\u8bed\u4e49\u8868\u8fbe\u80fd\u529b\u3002\n2. \u91c7\u7528DPCNN\u7f51\u7edc\u7ed3\u6784\u5bf9\u6587\u672c\u7279\u5f81\u8fdb\u884c\u6df1\u5ea6\u63d0\u53d6\uff0c\u4f18\u5316\u6fc0\u6d3b\u51fd\u6570\u5e76\u52a8\u6001\u8c03\u6574\u5b66\u4e60\u7387\u4ee5\u63d0\u5347\u6a21\u578b\u8868\u73b0\u3002\n3. \u4e0e\u591a\u79cd\u4e3b\u6d41\u6a21\u578b\u8fdb\u884c\u5bf9\u6bd4\u5b9e\u9a8c\uff0c\u9a8c\u8bc1\u6240\u63d0\u6a21\u578b\u7684\u6709\u6548\u6027\u3002", "objective": "1. \u63d0\u9ad8\u8b66\u60c5\u6587\u672c\u5927\u89c4\u6a21\u81ea\u52a8\u5316\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002\n2. \u9488\u5bf9\u73b0\u6709\u6a21\u578b\u7684\u4e0d\u8db3\uff0c\u8bbe\u8ba1\u80fd\u591f\u66f4\u597d\u63d0\u53d6\u6587\u672c\u7279\u5f81\u7684\u5206\u7c7b\u6a21\u578b\u3002", "results": "\u6240\u63d0\u51fa\u7684BERT-DPCNN\u6a21\u578b\u5728\u8b66\u60c5\u6587\u672c\u4e8c\u5206\u7c7b\u548c\u591a\u5206\u7c7b\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u4e1a\u5185\u8f83\u9ad8\u7684\u51c6\u786e\u7387\uff0c\u5168\u9762\u4f18\u4e8e\u73b0\u6709\u7684BERT\u7cfb\u5217\u7b49\u6a21\u578b\u3002"}


author: 韩普;王志伟;杜文文
date: 2025年4期
keywords: 法律文本分类;深度图神经网络;细粒度分类;解耦合;注意力扩散
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 基于深度注意力扩散图神经网络的法律文本细粒度分类方法
created_at: 2025-09-18 14:11:58.700772
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA0MDEyGghkNml0bWZpOQ%3D%3D

summary:
[cell repeats an earlier one in this file: 467 characters]

parsed_summary:
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author: 高瑜蔚;王兆钧;胡良霖
date: 2025年2期
keywords: 科学数据集;门控机制;词性特征;长文本;文本分类
quote: 0
source: 《北京邮电大学学报》
site: 万方
title: 面向科学数据集决策影响力评价的长文本分类方法
created_at: 2025-09-18 14:11:58.701224
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2JqeWRkeDIwMjUwMjAwMhoIZDZpdG1maTk%3D

summary:
[cell repeats an earlier one in this file: 392 characters]

parsed_summary:
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author: 黄靖;陶竹林;杜晓宇
date: 2025年7期
keywords: 多标签文本分类;图卷积网络;注意力机制;动态图
quote: 0
source: 《软件学报》
site: 万方
title: 基于特征融合动态图网络的多标签文本分类算法
created_at: 2025-09-18 14:11:58.701673
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTUaCGQ2aXRtZmk5

summary:
[cell repeats an earlier one in this file: 296 characters]

parsed_summary:
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author: 李倩;赵彦云;燕雪倩
date: 2025年3期
keywords: 新能源;产业政策;实施效果;文本分类;面板分位数回归模型
quote: 0
source: 《干旱区资源与环境》
site: 万方
title: 新能源产业政策文本分类及实施效果差异性研究
created_at: 2025-09-18 14:11:58.702147
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdocXp5eWhqMjAyNTAzMDAxGghkNml0bWZpOQ%3D%3D

summary:
[cell repeats an earlier one in this file: 349 characters]

parsed_summary:
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author: 谌文佳;杨琳;李金林
date: 2025年2期
keywords: 意图识别;医疗问答;语义相似度分类;深度学习
quote: 1
source: 《数据分析与知识发现》
site: 万方
title: 嵌入意图识别的医疗健康问答文本语义分类模型
created_at: 2025-09-18 14:11:58.702600
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGghkNml0bWZpOQ%3D%3D

summary:
[cell repeats an earlier one in this file: 452 characters]

parsed_summary:
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author: 王友卫;刘奥;凤丽洲
date: 2025年5期
keywords: 计算机应用;情感分类;知识蒸馏;数据增强;历史评论
quote: 0
source: 《吉林大学学报（工学版）》
site: 万方
title: 基于知识蒸馏和评论时间的文本情感分类新方法
created_at: 2025-09-18 14:11:58.703075
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoIZDZpdG1maTk%3D

summary:
[cell repeats an earlier one in this file: 349 characters]

parsed_summary:
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author: 陈敏;王雷春;徐瑞
date: 2025年2期
keywords: 自然语言处理;文本分类;新闻主题;XLNet;对比学习
quote: 1
source: 《郑州大学学报（理学版）》
site: 万方
title: 基于XLNet和多粒度对比学习的新闻主题文本分类方法
created_at: 2025-09-18 14:11:58.703538
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwMxoIZDZpdG1maTk%3D

summary:
[cell repeats an earlier one in this file: 397 characters]

parsed_summary:
  {"background": "\u65b0\u95fb\u4e3b\u9898\u6587\u672c\u901a\u5e38\u7b80\u77ed\u4f46\u4fe1\u606f\u5bc6\u5ea6\u9ad8\uff0c\u73b0\u6709\u65b9\u6cd5\u5bf9\u8bcd\u7ea7\u548c\u53e5\u7ea7\u7279\u5f81\u7684\u5173\u8054\u6316\u6398\u4e0d\u8db3\uff0c\u672a\u80fd\u5145\u5206\u6355\u6349\u591a\u7c92\u5ea6\u7279\u5f81\u95f4\u7684\u4f9d\u8d56\uff0c\u5bf9\u6a21\u578b\u8868\u8fbe\u80fd\u529b\u6709\u9650\u5236\u3002", "contribution": "1. \u63d0\u51fa\u7ed3\u5408XLNet\u4e0e\u591a\u7c92\u5ea6\u5bf9\u6bd4\u5b66\u4e60\u7684\u65b0\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u6709\u6548\u6355\u6349\u8bcd\u7c92\u5ea6\u4e0e\u53e5\u7c92\u5ea6\u95f4\u7684\u5173\u8054\u3002\n2. \u901a\u8fc7R-Drop\u7b56\u7565\u548c\u591a\u7c92\u5ea6\u7279\u5f81\u5bf9\u6bd4\uff0c\u63d0\u5347\u4e86\u6a21\u578b\u7684\u8868\u8fbe\u80fd\u529b\u548c\u5206\u7c7b\u6027\u80fd\u3002\n3. \u5728\u591a\u4e2a\u4e3b\u6d41\u65b0\u95fb\u6570\u636e\u96c6\u4e0a\u53d6\u5f97\u663e\u8457\u6548\u679c\uff0c\u62d3\u5c55\u4e86\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u7684\u65b0\u601d\u8def\u3002", "method": "1. \u5229\u7528XLNet\u63d0\u53d6\u6587\u672c\u8bcd\u7c92\u5ea6\u548c\u53e5\u7c92\u5ea6\u7279\u5f81\u53ca\u5176\u6f5c\u5728\u5173\u7cfb\u3002\n2. \u91c7\u7528\u591a\u7c92\u5ea6\u5bf9\u6bd4\u5b66\u4e60\u548cR-Drop\u7b56\u7565\uff0c\u751f\u6210\u6b63\u8d1f\u6837\u672c\u5bf9\uff0c\u9488\u5bf9\u8bcd-\u8bcd\u3001\u8bcd-\u53e5\u3001\u53e5-\u53e5\u7b49\u591a\u7c92\u5ea6\u5173\u7cfb\u5206\u522b\u8fdb\u884c\u7279\u5f81\u76f8\u4f3c\u5ea6\u5b66\u4e60\uff0c\u589e\u5f3a\u6a21\u578b\u5bf9\u591a\u7c92\u5ea6\u7279\u5f81\u95f4\u5173\u8054\u7684\u6355\u6349\u3002", "objective": "1. \u6df1\u5165\u6316\u6398\u65b0\u95fb\u4e3b\u9898\u6587\u672c\u4e2d\u8bcd\u5411\u91cf\u4e0e\u53e5\u5411\u91cf\u7684\u4f9d\u8d56\u5173\u7cfb\u3002\n2. \u63d0\u5347\u65b0\u95fb\u4e3b\u9898\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u6a21\u578b\u8868\u8fbe\u80fd\u529b\u3002", "results": "\u6240\u63d0\u65b9\u6cd5\u5728THUCNews\u3001Toutiao\u548cSHNews\u6570\u636e\u96c6\u4e0a\u5747\u8d85\u8d8a\u57fa\u51c6\u6a21\u578b\uff0cF1\u503c\u5206\u522b\u8fbe\u523093.88%\u300190.08%\u300187.35%\uff0c\u51c6\u786e\u7387\u548cF1\u5206\u6570\u5747\u6709\u5927\u5e45\u63d0\u5347\uff0c\u9a8c\u8bc1\u4e86\u591a\u7c92\u5ea6\u7279\u5f81\u5bf9\u6bd4\u5b66\u4e60\u65b9\u6cd5\u7684\u6709\u6548\u6027\u3002"}


author: 李文博;高盛祥;张勇丙
keywords: 跨语言文本分类;自适应迁移学习;自适应方法;零样本;自注意力
source: 昆明理工大学学报(自然科学版)
site: 维普
title: 基于注意力自适应迁移的零样本跨语言文本分类方法
created_at: 2025-09-18 14:11:58.703986
original_link:
  https://www.cqvip.com/doc/journal/3509792230?sign=b21231be300857d56f04d612894c19a03765ef57c9786dbff389e40567b709a8&expireTime=1789711694676&resourceId=3509792230

summary:
  零样本跨语言文本分类任务是指仅依赖源语言标注数据训练模型,并将其迁移到目标语言上,不需要目标语言的标注数据.而传统的跨语言文本分类方法通常需要一定规模的目标语言标注数据,且在跨语言迁移过程中往往忽略以类别标签相关联的关键词,导致跨语言迁移效果不佳.针对以上问题,提出了一种基于注意力自适应迁移的零样本跨语言文本分类方法,在完全不依赖目标语言标注数据的情况下,通过对种子词重要性建模和自适应迁移解决零样本跨语言文本分类的难题.首先,基于文本的词分布特征在源语言上抽取种子词,并对其进行重要性建模,生成重要性矩阵.在此基础上,利用大规模源语言标注数据训练教师模型,在训练过程中通过种子词的词概率分布进一步强调关键性信息.其次,将教师模型捕获的重要词通过跨双语词典映射关系映射到目标语言上.再次,利用教师模型为目标语言中包含种子词的无标注数据生成伪标签,这些伪标签构成了学生模型的初始训练数据,并以此训练学生.最后,学生模型进一步对目标语言的无标注数据进行预测,生成新的标注数据并扩充训练集,通过迭代优化最终得到用于目标语言分类任务的学生模型.实验表明,提出的方法在MLDoc和CLS数据集上与基线模型对比,准确率分别提高了10.5%、6.7%.

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author: 郑诚;纪子威
source: 小型微型计算机系统
site: 维普
title: 结合对比学习和提示调优的层次文本分类方法
created_at: 2025-09-18 14:11:58.704467
original_link:
  https://www.cqvip.com/doc/journal/3509807507?sign=010bba833e7b7a7ba8b3b9bd69a600d54a58c86277f981d5727a9d535f4fbdca&expireTime=1789711694676&resourceId=3509807507

summary:
  层次文本分类是文本分类领域一项重要的子任务,但复杂的标签层次结构使其具有挑战性.目前最先进的方法通过预训练语言模型对文本进行编码,并结合图编码器处理标签结构信息.但是,独立建模文本和标签信息可能导致信息利用率不高,预训练和微调阶段的不同策略也限制了模型挖掘预训练模型的知识.本文提出一种结合对比学习和提示调优的层次文本分类方法,将层次标签信息嵌入到文本编码过程,同时设计提示模板整合标签信息,捕获文本与标签的关联性,弥合预训练模型与下游任务间的差距.借助对比学习,根据标签信息生成正样本,强化模型对关键特征的学习与保留,使模型有效指导文本特征表示的学习.在两个公开数据集上的大量实验表明了方法的有效性.

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author: 张典;刘畅;陈雯柏;缪祎晟;吴华瑞
source: 中国农机化学报
site: 维普
title: 融合注意力机制的MacBERT-DPCNN农业文本分类模型
created_at: 2025-09-18 14:11:58.704941
original_link:
  https://www.cqvip.com/doc/journal/3506615314?sign=50aad51282965c5acf1a2795d142179b441cd7df84be1c15d5d489bbe33246c9&expireTime=1789711694676&resourceId=3506615314

summary:
[cell repeats an earlier one in this file: 377 characters]

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author: 李晨;刘纳;郑国风;杨杰;道路
source: 现代电子技术
site: 维普
title: 融合双通道特征信息的医疗短文本分类模型
created_at: 2025-09-18 14:11:58.705389
original_link:
  https://www.cqvip.com/doc/journal/3502600080?sign=94b2f5539dd3c75f493562765fcdf2eb9dc9cfa3367cec5c1186ed788a0c8001&expireTime=1789711694676&resourceId=3502600080

summary:
[cell repeats an earlier one in this file: 271 characters]

parsed_summary:
  {"background": "\u533b\u7597\u77ed\u6587\u672c\u5b58\u5728\u7279\u5f81\u7a00\u758f\u548c\u8bed\u4e49\u6b67\u4e49\u7684\u95ee\u9898\uff0c\u5bfc\u81f4\u4f20\u7edf\u65b9\u6cd5\u96be\u4ee5\u5145\u5206\u63d0\u53d6\u548c\u5229\u7528\u7279\u5f81\uff0c\u5f71\u54cd\u5206\u7c7b\u6548\u679c\u3002", "contribution": "1. \u63d0\u51fa\u878d\u5408\u53cc\u901a\u9053\u7279\u5f81\u7684\u6a21\u578b\uff0c\u7efc\u5408\u5229\u7528\u5168\u5c40\u548c\u5c40\u90e8\u7279\u5f81\u4fe1\u606f\uff0c\u6709\u6548\u7f13\u89e3\u533b\u7597\u77ed\u6587\u672c\u7684\u7279\u5f81\u8868\u8fbe\u96be\u9898\u3002\n2. \u5f15\u5165\u5bf9\u6297\u8bad\u7ec3\u65b9\u6cd5\u589e\u5f3a\u6a21\u578b\u9c81\u68d2\u6027\u548c\u6cdb\u5316\u80fd\u529b\uff0c\u4f18\u4e8e\u73b0\u6709\u4e3b\u6d41\u6a21\u578b\u3002", "method": "1. \u5229\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u751f\u6210\u52a8\u6001\u8bcd\u5411\u91cf\u4e30\u5bcc\u8bed\u4e49\u8868\u793a\u3002\n2. \u91c7\u7528BiLSTM\u63d0\u53d6\u5168\u5c40\u6587\u672c\u7279\u5f81\uff0cDPECNN\u63d0\u53d6\u6df1\u5c42\u6b21\u5c40\u90e8\u7279\u5f81\u3002\n3. \u5e94\u7528FGM\u5bf9\u6297\u8bad\u7ec3\u63d0\u5347\u6a21\u578b\u9c81\u68d2\u6027\u53ca\u6cdb\u5316\u80fd\u529b\u3002\n4. \u5c06\u53cc\u901a\u9053\u7279\u5f81\u8fdb\u884c\u878d\u5408\u83b7\u5f97\u6700\u7ec8\u6587\u672c\u8868\u793a\u7528\u4e8e\u5206\u7c7b\u3002", "objective": "1. \u63d0\u9ad8\u533b\u7597\u77ed\u6587\u672c\u5206\u7c7b\u51c6\u786e\u6027\uff0c\u7f13\u89e3\u7279\u5f81\u7a00\u758f\u548c\u8bed\u4e49\u6b67\u4e49\u5e26\u6765\u7684\u8d1f\u9762\u5f71\u54cd\u3002\n2. \u6784\u5efa\u80fd\u591f\u878d\u5408\u591a\u79cd\u7279\u5f81\u4fe1\u606f\u4e14\u5177\u6709\u5f3a\u9c81\u68d2\u6027\u7684\u5206\u7c7b\u6a21\u578b\u3002", "results": "\u5728\u533b\u7597\u53ca\u901a\u7528\u9886\u57df\u77ed\u6587\u672c\u6570\u636e\u96c6\u4e0a\uff0cEBDF\u6a21\u578b\u51c6\u786e\u7387\u63d0\u5347\u7ea60.57%~6.16%\uff0cF1\u503c\u63d0\u9ad8\u7ea60.65%~5.80%\u3002"}


author: 余明峰;秦永彬;黄瑞章;陈艳平;林川
source: 计算机应用
site: 维普
title: 基于对比学习增强双注意力机制的多标签文本分类方法
created_at: 2025-09-18 14:11:58.705850
original_link:
  https://www.cqvip.com/doc/journal/3505889201?sign=0924442a495c550a7d3e1736cc7fee9ff4da6374701ac5ab9e5fdb063351c1e8&expireTime=1789711694676&resourceId=3505889201

summary:
[cell repeats an earlier one in this file: 522 characters]

parsed_summary:
  {"background": "\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\uff0c\u73b0\u6709\u57fa\u4e8e\u6ce8\u610f\u529b\u673a\u5236\u7684\u65b9\u6cd5\u96be\u4ee5\u5145\u5206\u5efa\u6a21\u6587\u672c\u4e4b\u95f4\u590d\u6742\u7684\u4f9d\u8d56\u5173\u7cfb\uff0c\u5c24\u5176\u662f\u5728\u4e3b\u9898\u3001\u5185\u5bb9\u548c\u7ed3\u6784\u5c42\u9762\u3002", "contribution": "1. \u521b\u65b0\u6027\u5730\u7ed3\u5408\u5bf9\u6bd4\u5b66\u4e60\u4e0e\u53cc\u6ce8\u610f\u529b\u673a\u5236\uff0c\u589e\u5f3a\u6587\u672c\u8868\u793a\u80fd\u529b\uff0c\u6709\u6548\u6355\u6349\u6587\u672c\u95f4\u590d\u6742\u4f9d\u8d56\u30022. \u663e\u8457\u63d0\u5347\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u4e2d\u7684\u6027\u80fd\uff0c\u5728\u591a\u4e2a\u771f\u5b9e\u4e16\u754c\u6570\u636e\u96c6\u4e0a\u83b7\u5f97\u4f18\u5f02\u7ed3\u679c\u3002", "method": "1. \u6784\u5efa\u53cc\u6ce8\u610f\u529b\u673a\u5236\uff0c\u5206\u522b\u63d0\u53d6\u81ea\u6ce8\u610f\u529b\u548c\u6807\u7b7e\u6ce8\u610f\u529b\u4e0b\u7684\u6587\u672c\u8868\u793a\u5e76\u878d\u5408\uff0c\u5b9e\u73b0\u7ed3\u6784\u4e0e\u8bed\u4e49\u7279\u5f81\u7684\u5168\u9762\u6355\u83b7\u30022. \u5f15\u5165\u5bf9\u6bd4\u5b66\u4e60\u76ee\u6807\uff0c\u901a\u8fc7\u6807\u7b7e\u5f15\u5bfc\u7684\u6587\u672c\u76f8\u4f3c\u5ea6\u76d1\u7763\uff0c\u63d0\u5347\u6587\u672c\u8868\u793a\u5bf9\u4e3b\u9898\u3001\u5185\u5bb9\u548c\u7ed3\u6784\u7b49\u591a\u5c42\u6b21\u4f9d\u8d56\u7684\u611f\u77e5\u80fd\u529b\u30023. \u4f7f\u7528\u524d\u9988\u795e\u7ecf\u7f51\u7edc\u4f5c\u4e3a\u6700\u7ec8\u5206\u7c7b\u5668\u8fdb\u884c\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u3002", "objective": "1. \u63d0\u9ad8\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\uff0c\u901a\u8fc7\u66f4\u6709\u6548\u5730\u6355\u6349\u6587\u672c\u4e0e\u6807\u7b7e\u4e4b\u95f4\u4ee5\u53ca\u6587\u672c\u5185\u90e8\u7684\u590d\u6742\u5173\u8054\u30022. \u8bbe\u8ba1\u673a\u5236\u4ee5\u589e\u5f3a\u6a21\u578b\u5bf9\u4e8e\u6587\u672c\u6df1\u5c42\u8bed\u4e49\u548c\u7ed3\u6784\u7279\u5f81\u7684\u8868\u8fbe\u80fd\u529b\u3002", "results": "\u63d0\u51fa\u7684\u65b9\u6cd5\u5728EUR-Lex\u4e0eReuters-21578\u7b49\u57fa\u51c6\u6570\u636e\u96c6\u4e0a\uff0cnDCG@5\u6307\u6807\u5206\u522b\u8f83\u73b0\u6709\u65b9\u6cd5\u63d0\u53471.81\u548c0.86\u4e2a\u767e\u5206\u70b9\uff0c\u5e76\u5728AAPD\u548cRCV1\u6570\u636e\u96c6\u4e0a\u53d6\u5f97\u6709\u7ade\u4e89\u529b\u7684\u5206\u7c7b\u6027\u80fd\u3002"}


author: 郑诚;李鹏飞
keywords: 特征融合;超图;SenticNet词库;自然语言处理;文本分类
source: 计算机工程
site: 维普
title: 基于双超图神经网络特征融合的文本分类
created_at: 2025-09-18 14:11:58.706320
original_link:
  https://www.cqvip.com/doc/journal/3501748678?sign=790e6bf79930ecc14627901ae515ae929419bc8319a656a9c075b63e91151eb8&expireTime=1789711694676&resourceId=3501748678

summary:
[cell repeats an earlier one in this file: 315 characters]

parsed_summary:
  {"background": "\u5df2\u6709\u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u56e0\u56fe\u7ed3\u6784\u5c40\u9650\u5f80\u5f80\u96be\u4ee5\u6355\u6349\u5355\u8bcd\u4e4b\u95f4\u7684\u9ad8\u9636\u8bed\u4e49\u5173\u7cfb\uff0c\u4e5f\u96be\u4ee5\u6709\u6548\u63d0\u53d6\u6587\u672c\u4e2d\u7684\u5173\u952e\u8bed\u4e49\u4fe1\u606f\uff0c\u5f71\u54cd\u77ed\u6587\u672c\u5206\u7c7b\u6548\u679c\u3002", "contribution": "1. \u63d0\u51fa\u4e86\u878d\u5408\u7ed3\u6784\u8d85\u56fe\u548c\u8bed\u4e49\u8d85\u56fe\u7684\u53cc\u8d85\u56fe\u5377\u79ef\u7f51\u7edc\uff0c\u6709\u6548\u6355\u6349\u9ad8\u9636\u548c\u5173\u952e\u8bed\u4e49\u4fe1\u606f\u3002\n2. \u521b\u65b0\u6027\u5730\u5229\u7528\u5916\u90e8\u77e5\u8bc6\uff08SenticNet\uff09\u589e\u5f3a\u77ed\u6587\u672c\u8bed\u4e49\u8868\u8fbe\uff0c\u63d0\u9ad8\u4e86\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\u3002\n3. \u8bc1\u660e\u4e86\u901a\u8fc7\u6ce8\u610f\u529b\u673a\u5236\u878d\u5408\u591a\u6e90\u8bed\u4e49\u7279\u5f81\u80fd\u663e\u8457\u63d0\u5347\u6587\u672c\u5206\u7c7b\u6548\u679c\u3002", "method": "1. \u5c06\u539f\u59cb\u6587\u672c\u5efa\u6a21\u4e3a\u8d85\u56fe\u4ee5\u6355\u83b7\u5355\u8bcd\u95f4\u590d\u6742\u5173\u7cfb\u3002\n2. \u5229\u7528SenticNet\u8bcd\u5e93\u5f15\u5165\u5916\u90e8\u77e5\u8bc6\uff0c\u6784\u5efa\u8bed\u4e49\u8d85\u56fe\u5bf9\u6587\u672c\u8fdb\u884c\u8bed\u4e49\u589e\u5f3a\u3002\n3. \u4f7f\u7528\u8d85\u56fe\u5377\u79ef\u5bf9\u4e24\u7c7b\u8d85\u56fe\u5206\u522b\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\uff0c\u5e76\u901a\u8fc7\u6ce8\u610f\u529b\u673a\u5236\u878d\u5408\u53cc\u8d85\u56fe\u7279\u5f81\uff0c\u6700\u7ec8\u5b8c\u6210\u6587\u672c\u5206\u7c7b\u3002", "objective": "1. \u63d0\u5347\u6587\u672c\u5206\u7c7b\u6a21\u578b\u6355\u6349\u9ad8\u9636\u5355\u8bcd\u8bed\u4e49\u5173\u7cfb\u7684\u80fd\u529b\u3002\n2. \u589e\u5f3a\u6a21\u578b\u5bf9\u6587\u672c\u5173\u952e\u8bed\u4e49\u7684\u63d0\u53d6\u80fd\u529b\uff0c\u4ee5\u63d0\u5347\u77ed\u6587\u672c\u5206\u7c7b\u6548\u679c\u3002", "results": "\u57284\u4e2a\u6587\u672c\u5206\u7c7b\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u4e2d\uff0c\u6240\u63d0\u6a21\u578b\u5728\u5206\u7c7b\u6027\u80fd\u4e0a\u663e\u8457\u4f18\u4e8e\u591a\u79cd\u57fa\u7ebf\u65b9\u6cd5\u3002"}


author: 张泽;王炯;王晓艺;聂凯峰;刘杰;周建设
keywords: 正则化更新;动态网络扩展;终身学习;文本分类
source: 小型微型计算机系统
site: 维普
title: 融合网络剪枝和动态扩展的终身文本分类方法
created_at: 2025-09-18 14:11:58.706776
original_link:
  https://www.cqvip.com/doc/journal/3500943458?sign=741d56c9bf83a0f4f5e34cafbee82ebd259b3d014267cab40fffa3b3fafb6c74&expireTime=1789711694676&resourceId=3500943458

summary:
[cell repeats an earlier one in this file: 357 characters]

parsed_summary:
  {"background": "\u6587\u672c\u4fe1\u606f\u6765\u6e90\u591a\u6837\uff0c\u5185\u5bb9\u4e0d\u65ad\u6269\u5c55\uff0c\u5bfc\u81f4\u795e\u7ecf\u7f51\u7edc\u6587\u672c\u5206\u7c7b\u5668\u5728\u65b0\u9886\u57df\u5b66\u4e60\u65f6\u5e38\u51fa\u73b0\u65e7\u77e5\u8bc6\u9057\u5fd8\u95ee\u9898\uff0c\u4e9f\u9700\u80fd\u6301\u7eed\u5b66\u4e60\u4e14\u4fdd\u6301\u77e5\u8bc6\u8fde\u7eed\u6027\u7684\u89e3\u51b3\u65b9\u6848\u3002", "contribution": "1. \u5b9e\u73b0\u4e86\u7ec8\u8eab\u6587\u672c\u5206\u7c7b\u5668\u5728\u6269\u5c55\u65b0\u77e5\u8bc6\u65f6\u6709\u6548\u907f\u514d\u707e\u96be\u9057\u5fd8\u30022. \u521b\u65b0\u5730\u878d\u5408\u7f51\u7edc\u526a\u679d\u3001\u6269\u5c55\u53ca\u53c2\u6570\u6b63\u5219\u5316\u673a\u5236\uff0c\u63d0\u5347\u4e86\u6a21\u578b\u6301\u7eed\u5b66\u4e60\u4e0e\u8fc1\u79fb\u80fd\u529b\u30023. \u5b9e\u8bc1\u5c55\u793a\u8be5\u6280\u672f\u5728\u591a\u9886\u57df\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u7684\u4f18\u8d8a\u8868\u73b0\uff0c\u6269\u5c55\u4e86\u7ec8\u8eab\u5b66\u4e60\u6587\u672c\u5206\u7c7b\u7684\u5b9e\u7528\u6027\u3002", "method": "1. \u5c06\u795e\u7ecf\u7f51\u7edc\u526a\u679d\u4e0e\u52a8\u6001\u7f51\u7edc\u6269\u5c55\u6280\u672f\u7ed3\u5408\uff0c\u7528\u4e8e\u8c03\u8282\u6a21\u578b\u5bb9\u91cf\u4ee5\u9002\u5e94\u65b0\u9886\u57df\u5b66\u4e60\u30022. \u91c7\u7528\u4e0d\u786e\u5b9a\u6027\u53c2\u6570\u6b63\u5219\u5316\u66f4\u65b0\u7b56\u7565\u4ee5\u589e\u5f3a\u65e7\u4efb\u52a1\u77e5\u8bc6\u4fdd\u7559\u80fd\u529b\u5e76\u5b9e\u73b0\u4efb\u52a1\u95f4\u77e5\u8bc6\u8fc1\u79fb\u30023. \u5728\u4e94\u4e2a\u4e3b\u6d41\u6587\u672c\u5206\u7c7b\u6570\u636e\u96c6\u4e0a\u5f00\u5c55\u7ec8\u8eab\u5b66\u4e60\u5b9e\u9a8c\uff0c\u6bd4\u8f83\u4e0eBERT\u53ca\u5176\u5b83\u65b9\u6cd5\u7684\u6027\u80fd\u3002", "objective": "1. \u63d0\u51fa\u4e00\u79cd\u80fd\u5728\u6269\u5c55\u77e5\u8bc6\u80cc\u666f\u4e0b\u6301\u7eed\u5b66\u4e60\u4e14\u907f\u514d\u707e\u96be\u9057\u5fd8\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u30022. \u9a8c\u8bc1\u65b9\u6cd5\u5728\u591a\u9886\u57df\u6301\u7eed\u5b66\u4e60\u4efb\u52a1\u4e2d\u7684\u6709\u6548\u6027\u3002", "results": "\u63d0\u51fa\u7684\u878d\u5408\u7f51\u7edc\u526a\u679d\u548c\u52a8\u6001\u6269\u5c55\u65b9\u6cd5\u5728\u4e94\u5927\u6570\u636e\u96c6\u4e0a\u7684\u7ec8\u8eab\u5b66\u4e60\u5b9e\u9a8c\u6027\u80fd\u4f18\u4e8eBERT\u53ca\u4e3b\u8981\u5bf9\u6bd4\u65b9\u6cd5\uff0c\u51c6\u786e\u7387\u63d0\u5347\u7ea610%\u3002"}


author: 李静娟;邢凯;聂挺
keywords: 数据分布;最优传输理论;预训练;文本分类
source: 计算机应用与软件
site: 维普
title: 基于最优传输理论和格兰杰因果关系检验的文本分类优化
created_at: 2025-09-18 14:11:58.707231
original_link:
  https://www.cqvip.com/doc/journal/3502552353?sign=d274a06c0c133bc5c26bf3bbe251f2d405e738520065d8c5d8a2e077285543ca&expireTime=1789711694676&resourceId=3502552353

summary:
[cell repeats an earlier one in this file: 285 characters]

parsed_summary:
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author: 杜润隆;谭柯鑫;高添;韩郅;徐云凤
keywords: TextRCNN;施工方案;Albert;段落文本
source: 水利水电技术(中英文)
site: 维普
title: 施工方案类别及文本分类模型实现研究分类
created_at: 2025-09-18 14:11:58.707705
original_link:
  https://www.cqvip.com/doc/journal/3501769600?sign=a36f37aacc42e7a5f17d4db4afc8e2bc7556e1e9334d750206a6668cc584a79b&expireTime=1789711694676&resourceId=3501769600

summary:
[cell repeats an earlier one in this file: 383 characters]

parsed_summary:
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author: 曾谁飞;孟瑶;刘静
keywords: 注意力机制;图神经网络;文本分类;图数据
source: 电信科学
site: 维普
title: 基于GNN与注意力机制的文本分类模型
created_at: 2025-09-18 14:11:58.708170
original_link:
  https://www.cqvip.com/doc/journal/3501556210?sign=48a7e159aa8ef51345183c2d241a761a467d30e45e868a362a4ad3fefe20ac7b&expireTime=1789711694676&resourceId=3501556210

summary:
[cell repeats an earlier one in this file: 487 characters]

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author: Elijah Tomin;Marina Solnyshkina;Elzara Gafiyatova;Albina Galiakhmetova
date: 2023
original_link: https://ieeexplore.ieee.org/document/10387935/
site: ieeeXplore
title: Automatic Text Classification as Relevance Measure for Russian School Physics Texts
created_at: 2025-09-18 14:11:58.708633
summary:
  The research tests two text classification methods applied to lexical features of Russian physics schoolbook texts. The research findings indicate that text classification methods can be implemented effectively to identify if a text relevant for a specific topic or a year of studying. We use and describe results via classical methods to validate the classification models.

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author: Shoujin Wang;Chengyu Duan;Yuanjiao Yang
date: 2022
original_link: https://ieeexplore.ieee.org/document/10047503/
site: ieeeXplore
title: Weakly Supervised Chinese Short Text Classification Algorithm Based on ConWea Model
created_at: 2025-09-18 14:11:58.709105
summary:
  The version sort algorithm based on full supervision needs to use a large amount of label data, and the labeling task of text data is time-consuming and difficult to label. Finally, use high-quality pseudo-label data to train a Chinese brief version systematics model. Experiment on the THUCNews news headlines dataset. The test outcome illustrate the performance of this algorithm is slightly stronger than the mainstream semi-supervised classification algorithm when only a small amount of label data is used, and it is not inferior to the general fully supervised classification algorithm. which provides a better solution for unlabeled data classification tasks.

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author: Gang Jin
date: 2022
original_link: https://ieeexplore.ieee.org/document/10036794/
site: ieeeXplore
created_at: 2025-09-18 14:11:58.709587
summary:
  How to use advanced deep learning technology to build an effective and powerful text classification model, extract text semantic attributes and achieve good classification results on large-scale test datasets has always been the main problem and severe challenge of text classification tasks. The purpose of this paper is to study the application optimization of NLP system in text semantics and text classification based on deep learning technology. A WC-GCN classification algorithm combining GCN and Attention mechanism is proposed. The related tasks of text classification are discussed, including three typical text classification tasks, including sentiment analysis, topic analysis, and news classification. The algorithm model proposed in this paper is verified from the experimental point of view. Compared with other deep learning-based text classification methods, the proposed algorithm model performs better on the public datasets of three text classification tasks. The news text also achieved a high accuracy rate.

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title:
  Application Optimization of NLP System under Deep Learning Technology in Text Semantics and Text Classification


author: Bihui Yu;Chen Deng;Liping Bu
date: 2022
original_link: https://ieeexplore.ieee.org/document/9849586/
site: ieeeXplore
title: Policy Text Classification Algorithm Based on Bert
created_at: 2025-09-18 14:11:58.710076
summary:
  With the development of the Internet, the policy text classification model of deep learning is used to improve the effect of policy text classification, and to play and use the huge value contained in the policy text. In order to more accurately determine the policy field described by the text, a BERT-based policy text classification algorithm is proposed. First, the algorithm uses the BERT (Bidirectional Encoder Representations from Transformers) pre-trained language model to vectorize the sentence-level feature of the policy field text, and then the obtained feature vector is input into the classifier for classification, and finally the homology policy field is used. The text data set is verified. The experimental results show that the classification of the trained model on the test set recorded the highest F1 value of 93.25%. It is nearly 6% higher than the classification task of the BERT model for the MRPC task. Therefore, the proposed policy domain text classification algorithm can more accurately and efficiently judge the domain of the policy text, which is helpful for further analysis of the text data in the policy domain and extract more valuable information.

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author: Fatih Gürcan
date: 2018
original_link: https://ieeexplore.ieee.org/document/8567307/
site: ieeeXplore
title: Multi-Class Classification of Turkish Texts with Machine Learning Algorithms
created_at: 2025-09-18 14:11:58.710550
summary:
  The problem of text classification is the process of supervised assignment of text documents to one or more predefined categories or classes according to the content of the processed texts with natural language processing methods. Text classification applications are actively used in various fields such as categorization of social interactions, web pages and news texts, optimization of search engines, extracting information, and automatically processing e-mails. In this context, it is aimed to classify Turkish texts with methods based on supervised machine learning. In this context, the classification success of supervised learning models on Turkish texts was analyzed with different parameters. These models have been tested for classification of news texts on five predefined classes (economy, politics, sport, health, and technology) and the system was trained with different number of training documents and the classification process was carried out. In this context, the classification performances of Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, and Decision Trees algorithms on Turkish news texts are compared and interpreted in the light of the results obtained with different parameters. As a result of the study, the procedure with the best classification success was the Multinomial Naïve Bayes algorithm with a classification success of about 90%. These results show that the Naïve Bayes probability model can be used as an effective classifier method in classifying Turkish texts compared to other methods. In this context, it is envisaged that the proposed methodology could be applied to Turkish texts on different web platforms (social networks, forums, communication networks, etc.) for different purposes.

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author: Zhenzhong Li;Wenqian Shang;Menghan Yan
date: 2016
original_link: https://ieeexplore.ieee.org/document/7550929/
site: ieeeXplore
title: News text classification model based on topic model
created_at: 2025-09-18 14:11:58.711009
summary:
  In modern society, some famous news websites such as Sina and Times server to provide information every day for millions of users. But with the continuous development of information technology, the amount of disorder data is increasing. How to organize the text and make automatically text classification has become a challenge. The traditional manual classification of news text not only consumes a lot of human and financial resources, but also hardly achieved classification task quickly. In this paper, the paper mainly makes a research about the news text classification. It proposes a news text classification model based on Latent Dirichlet Allocation (LDA). Due to the dimension of the news texts is too high, this model uses topic model to make text dimension reduced and get features. At the same time, the paper also makes a research on Softmax regression algorithm to solve multi-class of text problems in our life and make it as model's classifier. The paper evaluates proposed model on a real news dataset and the result of the experiment shows the improved model performs relatively well. The model can effectively reduce the features dimension of the news text and get good classification results.

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author: Zhou Faguo;Zhang Fan;Yang Bingru;Yu Xingang
date: 2010
original_link: https://ieeexplore.ieee.org/document/5557448/
site: ieeeXplore
title: Research on Short Text Classification Algorithm Based on Statistics and Rules
created_at: 2025-09-18 14:11:58.711471
summary:
  In this paper, we introduced the overview of short text research and the short text classification firstly. On the foundation of several common used classic text classification algorithms, mainly according to the major feature extraction methods, the short text classification based on statistics and rules is proposed. Experiments show that this algorithm has better performance than other algorithms. In order to improve the recall rate of short text classification, two-steps classification method is put forward.

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author: Yanming Cheng;Zhigang Yu;Je Hu;Mingchuan Yang
date: 2022
original_link: https://ieeexplore.ieee.org/document/10009824/
site: ieeeXplore
title: A Chinese Short Text Classification Method Based on TF-IDF and Gradient Boosting Decision Tree
created_at: 2025-09-18 14:11:58.711932
summary:
  To solve the problem of feature extraction and semantic sparsity in Chinese short text classification, this paper uses TF-IDF algorithm to extract category keywords and uses the set of category keywords as the feature set of short text classification. Next, the weight of keyword features is obtained by calculating the maximum similarity between the category keywords and each word in the essay. Based on the weighted keyword feature vector set, the short text is represented by vectors. Finally, we use the GBDT algorithm to train the classifier for short text classification and carry out experiments to verify the effectiveness of this method in improving the classification effect.

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author: Xiao-Dan Zhang
date: 2010
original_link: https://ieeexplore.ieee.org/document/5529774/
site: ieeeXplore
title: A general decision layer text classification fusion model
created_at: 2025-09-18 14:11:58.712392
summary:
  An general decision layer text classification fusion model for higher precision, is proposed, which based on model theory of information fusion, and different classification algorithm of the feature layer fusion centre having different pre-processing, their classification results input into the decision layer fusion centre separately. And the final classification result output from decision layer fusion centre. KNN, SVM and BP Net are used in feature layer, and D-S Theory is used in decision layer. The model is realized in the experiment. From the experiment and contrast, the text classification fusion model can improve the classification precision effectively.

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author: Fang Miao;Pu Zhang;Libiao Jin;Hongda Wu
date: 2018
original_link: https://ieeexplore.ieee.org/document/8530178/
site: ieeeXplore
title: Chinese News Text Classification Based on Machine Learning Algorithm
created_at: 2025-09-18 14:11:58.712891
summary:
  Text classification is the key technology for mining and organizing text information, which is the process of determining the text types automatically according to the content. Based on machine learning algorithm, text classification system includes four processes, namely text pretreatment, text representation, classifier training and classification. In this paper, a Chinese news text classification system model is designed. And in the classifier training part, we separately chose and compared K-nearest Neighbor, Naive Bayes, and Support Vector Machine as our classification algorithm. Then, we tested and analyzed these classifiers with each other and finally got a conclusion. The experimental conclusion shows that the Chinese news text classification system can get satisfied results based on the machine learning algorithm.

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author: Peter Beidler;Mark Nguyen;Kevin Lybarger;Ola Holmberg;Eric Ford;John Kang
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.13706
pdf_url: https://arxiv.org/pdf/2509.13706
site: arxiv
created_at: 2025-09-18 14:11:58.713365
summary:
  Abstract:PURPOSE: Incident reports are an important tool for safety and quality improvement in healthcare, but manual review is time-consuming and requires subject matter expertise. Here we present a natural language processing (NLP) screening tool to detect high-severity incident reports in radiation oncology across two institutions.

  METHODS AND MATERIALS: We used two text datasets to train and evaluate our NLP models: 7,094 reports from our institution (Inst.), and 571 from IAEA SAFRON (SF), all of which had severity scores labeled by clinical content experts. We trained and evaluated two types of models: baseline support vector machines (SVM) and BlueBERT which is a large language model pretrained on PubMed abstracts and hospitalized patient data. We assessed for generalizability of our model in two ways. First, we evaluated models trained using Inst.-train on SF-test. Second, we trained a BlueBERT_TRANSFER model that was first fine-tuned on Inst.-train then on SF-train before testing on SF-test set. To further analyze model performance, we also examined a subset of 59 reports from our Inst. dataset, which were manually edited for clarity.

  RESULTS Classification performance on the Inst. test achieved AUROC 0.82 using SVM and 0.81 using BlueBERT. Without cross-institution transfer learning, performance on the SF test was limited to an AUROC of 0.42 using SVM and 0.56 using BlueBERT. BlueBERT_TRANSFER, which was fine-tuned on both datasets, improved the performance on SF test to AUROC 0.78. Performance of SVM, and BlueBERT_TRANSFER models on the manually curated Inst. reports (AUROC 0.85 and 0.74) was similar to human performance (AUROC 0.81).

  CONCLUSION: In summary, we successfully developed cross-institution NLP models on incident report text from radiation oncology centers. These models were able to detect high-severity reports similarly to humans on a curated dataset.

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title:
  Automated Triaging and Transfer Learning of Incident Learning Safety Reports Using Large Language Representational Models


author: Iyadh Ben Cheikh Larbi;Ajay Madhavan Ravichandran;Aljoscha Burchardt;Roland Roller
date: 17 September, 2025
original_link: https://arxiv.org/abs/2509.13696
pdf_url: https://arxiv.org/pdf/2509.13696
site: arxiv
title: Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes
created_at: 2025-09-18 14:11:58.713816
summary:
  Abstract:Large language models (LLMs) excel at text generation, but their ability to handle clinical classification tasks involving structured data, such as time series, remains underexplored. In this work, we adapt instruction-tuned LLMs using DSPy-based prompt optimization to process clinical notes and structured EHR inputs jointly. Our results show that this approach achieves performance on par with specialized multimodal systems while requiring less complexity and offering greater adaptability across tasks.

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author: Heng Zhang;Chengzhi Zhang
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12955
pdf_url: https://arxiv.org/pdf/2509.12955
site: arxiv
title: Automated Generation of Research Workflows from Academic Papers: A Full-text Mining Framework
created_at: 2025-09-18 14:11:58.714288
summary:
  Abstract:The automated generation of research workflows is essential for improving the reproducibility of research and accelerating the paradigm of "AI for Science". However, existing methods typically extract merely fragmented procedural components and thus fail to capture complete research workflows. To address this gap, we propose an end-to-end framework that generates comprehensive, structured research workflows by mining full-text academic papers. As a case study in the Natural Language Processing (NLP) domain, our paragraph-centric approach first employs Positive-Unlabeled (PU) Learning with SciBERT to identify workflow-descriptive paragraphs, achieving an F1-score of 0.9772. Subsequently, we utilize Flan-T5 with prompt learning to generate workflow phrases from these paragraphs, yielding ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.4543, 0.2877, and 0.4427, respectively. These phrases are then systematically categorized into data preparation, data processing, and data analysis stages using ChatGPT with few-shot learning, achieving a classification precision of 0.958. By mapping categorized phrases to their document locations in the documents, we finally generate readable visual flowcharts of the entire research workflows. This approach facilitates the analysis of workflows derived from an NLP corpus and reveals key methodological shifts over the past two decades, including the increasing emphasis on data analysis and the transition from feature engineering to ablation studies. Our work offers a validated technical framework for automated workflow generation, along with a novel, process-oriented perspective for the empirical investigation of evolving scientific paradigms. Source code and data are available at: this https URL.

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author: Allen Schmaltz
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12760
pdf_url: https://arxiv.org/pdf/2509.12760
site: arxiv
title: Similarity-Distance-Magnitude Activations
created_at: 2025-09-18 14:11:58.714745
summary:
  Abstract:We introduce a more robust and interpretable formulation of the standard softmax activation function commonly used with neural networks by adding Similarity (i.e., correctly predicted depth-matches into training) awareness and Distance-to-training-distribution awareness to the existing output Magnitude (i.e., decision-boundary) awareness. When used as the final-layer activation with language models, the resulting Similarity-Distance-Magnitude (SDM) activation function is more robust than the softmax function to co-variate shifts and out-of-distribution inputs in high-probability regions, and provides interpretability-by-exemplar via dense matching. Complementing the prediction-conditional estimates, the SDM activation enables a partitioning of the class-wise empirical CDFs to guard against low class-wise recall among selective classifications. These properties make it preferable for selective classification, even when considering post-hoc calibration methods over the softmax.

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author: Anmol Singhal Navya Singhal
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12611
pdf_url: https://arxiv.org/pdf/2509.12611
site: arxiv
created_at: 2025-09-18 14:11:58.715188
summary:
  Abstract:Financial news sentiment analysis is crucial for anticipating market movements. With the rise of AI techniques such as Large Language Models (LLMs), which demonstrate strong text understanding capabilities, there has been renewed interest in enhancing these systems. Existing methods, however, often struggle to capture the complex economic context of news and lack transparent reasoning, which undermines their reliability. We propose Analogy-Driven Financial Chain-of-Thought (AD-FCoT), a prompting framework that integrates analogical reasoning with chain-of-thought (CoT) prompting for sentiment prediction on historical financial news. AD-FCoT guides LLMs to draw parallels between new events and relevant historical scenarios with known outcomes, embedding these analogies into a structured, step-by-step reasoning chain. To our knowledge, this is among the first approaches to explicitly combine analogical examples with CoT reasoning in finance. Operating purely through prompting, AD-FCoT requires no additional training data or fine-tuning and leverages the model's internal financial knowledge to generate rationales that mirror human analytical reasoning. Experiments on thousands of news articles show that AD-FCoT outperforms strong baselines in sentiment classification accuracy and achieves substantially higher correlation with market returns. Its generated explanations also align with domain expertise, providing interpretable insights suitable for real-world financial analysis.

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title:
  Analogy-Driven Financial Chain-of-Thought (AD-FCoT): A Prompting Approach for Financial Sentiment Analysis


author: Yingxin Lai;Zitong Yu;Jun Wang;Linlin Shen;Yong Xu;Xiaochun Cao
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12546
pdf_url: https://arxiv.org/pdf/2509.12546
site: arxiv
title: Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection
created_at: 2025-09-18 14:11:58.715658
summary:
  Abstract:Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy,which we attribute to the ecological invalidity of training this http URL work introduces Agent4FaceForgery to address two fundamental problems: (1) how to capture the diverse intents and iterative processes of human forgery creation, and (2) how to model the complex, often adversarial, text-image interactions that accompany forgeries in social media. To solve this,we propose a multi-agent framework where LLM-poweredagents, equipped with profile and memory modules, simulate the forgery creation process. Crucially, these agents interact in a simulated social environment to generate samples labeled for nuanced text-image consistency, moving beyond simple binary classification. An Adaptive Rejection Sampling (ARS) mechanism ensures data quality and diversity. Extensive experiments validate that the data generated by our simulationdriven approach brings significant performance gains to detectors of multiple architectures, fully demonstrating the effectiveness and value of our framework.

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author: Jiayi He;Yangmin Huang;Qianyun Du;Xiangying Zhou;Zhiyang He;Jiaxue Hu;Xiaodong Tao;Lixian Lai
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12440
pdf_url: https://arxiv.org/pdf/2509.12440
site: arxiv
created_at: 2025-09-18 14:11:58.716126
summary:
  Abstract:The increasing deployment of Large Language Models (LLMs) in healthcare necessitates a rigorous evaluation of their factual reliability. However, existing benchmarks are often limited by narrow domains of data, failing to capture the complexity of real-world medical information. To address this critical gap, we introduce MedFact, a new and challenging benchmark for Chinese medical fact-checking. MedFact comprises 2,116 expert-annotated instances curated from diverse real-world texts, spanning 13 medical specialties, 8 fine-grained error types, 4 writing styles, and multiple difficulty levels. Its construction employs a hybrid AI-human framework where iterative expert feedback refines an AI-driven, multi-criteria filtering process, ensuring both high data quality and difficulty. We conduct a comprehensive evaluation of 20 leading LLMs, benchmarking their performance on veracity classification and error localization against a human expert baseline. Our results reveal that while models can often determine if a text contains an error, precisely localizing it remains a substantial challenge, with even top-performing models falling short of human performance. Furthermore, our analysis uncovers a frequent ``over-criticism'' phenomenon, a tendency for models to misidentify correct information as erroneous, which is exacerbated by advanced reasoning techniques such as multi-agent collaboration and inference-time scaling. By highlighting these critical challenges for deploying LLMs in medical applications, MedFact provides a robust resource to drive the development of more factually reliable and medically aware models.

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title:
  MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts


author: Min Zeng;Jingfei Sun;Xueyou Luo;Caiquan Liu;Shiqi Zhang;Li Xie;Xiaoxin Chen
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12108
pdf_url: https://arxiv.org/pdf/2509.12108
site: arxiv
title: GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models
created_at: 2025-09-18 14:11:58.716576
summary:
  Abstract:In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tuning (SFT) methods, although efficient in training, have limited performance ceiling and less solid theoretical foundation compared to RL. To address efficiency-capability trade-off, we propose the Guess-Think-Answer (GTA) framework that combines the efficiency of SFT with the capability gains of RL in a unified training paradigm. GTA works by having the model first produce a provisional guess (optimized via cross-entropy loss), then reflect on this guess before generating the final answer, with RL rewards shaping both the final output and the format of the entire GTA structure. This hybrid approach achieves both faster convergence than pure RL and higher performance ceiling than pure SFT. To mitigate gradient conflicts between the two training signals, we employ loss masking and gradient constraints. Empirical results on four text classification benchmarks demonstrate that GTA substantially accelerates convergence while outperforming both standalone SFT and RL baselines.

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author: Hafza Eman;Furqan Shaukat;Muhammad Hamza Zafar;Syed Muhammad Anwar
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.11714
pdf_url: https://arxiv.org/pdf/2509.11714
site: arxiv
created_at: 2025-09-18 14:11:58.717057
summary:
  Abstract:Objective: Lung cancer is a leading cause of cancer-related mortality worldwide, primarily due to delayed diagnosis and poor early detection. This study aims to develop a computer-aided diagnosis (CAD) system that leverages large vision-language models (VLMs) for the accurate detection and classification of pulmonary nodules in computed tomography (CT) scans.

  Methods: We propose an end-to-end CAD pipeline consisting of two modules: (i) a detection module (CADe) based on the Segment Anything Model 2 (SAM2), in which the standard visual prompt is replaced with a text prompt encoded by CLIP (Contrastive Language-Image Pretraining), and (ii) a diagnosis module (CADx) that calculates similarity scores between segmented nodules and radiomic features. To add clinical context, synthetic electronic medical records (EMRs) were generated using radiomic assessments by expert radiologists and combined with similarity scores for final classification. The method was tested on the publicly available LIDC-IDRI dataset (1,018 CT scans).

  Results: The proposed approach demonstrated strong performance in zero-shot lung nodule analysis. The CADe module achieved a Dice score of 0.92 and an IoU of 0.85 for nodule segmentation. The CADx module attained a specificity of 0.97 for malignancy classification, surpassing existing fully supervised methods.

  Conclusions: The integration of VLMs with radiomics and synthetic EMRs allows for accurate and clinically relevant CAD of pulmonary nodules in CT scans. The proposed system shows strong potential to enhance early lung cancer detection, increase diagnostic confidence, and improve patient management in routine clinical workflows.

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title:
  EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images


author: Md. Mithun Hossain;Sanjara;Md. Shakil Hossain;Sudipto Chaki
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.11604
pdf_url: https://arxiv.org/pdf/2509.11604
site: arxiv
title: Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification
created_at: 2025-09-18 14:11:58.717517
summary:
  Abstract:Entity-level sentiment classification involves identifying the sentiment polarity linked to specific entities within text. This task poses several challenges: effectively modeling the subtle and complex interactions between entities and their surrounding sentiment expressions; capturing dependencies that may span across sentences; and ensuring consistent sentiment predictions for multiple mentions of the same entity through coreference resolution. Additionally, linguistic phenomena such as negation, ambiguity, and overlapping opinions further complicate the analysis. These complexities make entity-level sentiment classification a difficult problem, especially in real-world, noisy textual data. To address these issues, we propose SpanEIT, a novel framework integrating dynamic span interaction and graph-aware memory mechanisms for enhanced entity-sentiment relational modeling. SpanEIT builds span-based representations for entities and candidate sentiment phrases, employs bidirectional attention for fine-grained interactions, and uses a graph attention network to capture syntactic and co-occurrence relations. A coreference-aware memory module ensures entity-level consistency across documents. Experiments on FSAD, BARU, and IMDB datasets show SpanEIT outperforms state-of-the-art transformer and hybrid baselines in accuracy and F1 scores. Ablation and interpretability analyses validate the effectiveness of our approach, underscoring its potential for fine-grained sentiment analysis in applications like social media monitoring and customer feedback analysis.

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author: Stringham OC, Moncayo S, Hill KGW, Toomes A, Mitchell L, Ross JV, Cassey P.
original_link: https://pubmed.ncbi.nlm.nih.gov/34242279/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8270201/pdf/pone.0254007.pdf
site: pubmed
title: Text classification to streamline online wildlife trade analyses.
created_at: 2025-09-18 14:11:58.717997
summary:
  Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.

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author: Shen K, Hao P, Li R.
original_link: https://pubmed.ncbi.nlm.nih.gov/32831821/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7428956/pdf/CIN2020-8879795.pdf
site: pubmed
title: A Compressive Sensing Model for Speeding Up Text Classification.
created_at: 2025-09-18 14:11:58.718456
summary:
  Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model has a low time and space complexity while training a text classifier, and the restricted isometry property (RIP) of CS ensures that pairwise distances between text features can be well preserved in the process of dimensionality reduction. In particular, by structural random matrices (SRMs), CS is free from computation and memory limitations in the construction of random projections. Experimental results demonstrate that CS effectively accelerates the text classification while hardly causing any accuracy loss.

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author: Baghdadi Y, Bourrée A, Robert A, Rey G, Gallay A, Zweigenbaum P, Grouin C, Fouillet A.
original_link: https://pubmed.ncbi.nlm.nih.gov/31522022/
site: pubmed
created_at: 2025-09-18 14:11:58.718929
summary:
  Background: Mortality surveillance is of fundamental importance to public health surveillance. The real-time recording of death certificates, thanks to Electronic Death Registration System (EDRS), provides valuable data for reactive mortality surveillance based on medical causes of death in free-text format. Reactive mortality surveillance is based on the monitoring of mortality syndromic groups (MSGs). An MSG is a cluster of medical causes of death (pathologies, syndromes or symptoms) that meets the objectives of early detection and impact assessment of public health events. The aim of this study is to implement and measure the performance of a rule-based method and two supervised models for automatic free-text cause of death classification from death certificates in order to implement them for routine surveillance.

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title:
  Automatic classification of free-text medical causes from death certificates for reactive mortality surveillance in France.


author: Knights D, Costello EK, Knight R.
original_link: https://pubmed.ncbi.nlm.nih.gov/21039646/
site: pubmed
title: Supervised classification of human microbiota.
created_at: 2025-09-18 14:11:58.719390
summary:
  Recent advances in DNA sequencing technology have allowed the collection of high-dimensional data from human-associated microbial communities on an unprecedented scale. A major goal of these studies is the identification of important groups of microorganisms that vary according to physiological or disease states in the host, but the incidence of rare taxa and the large numbers of taxa observed make that goal difficult to obtain using traditional approaches. Fortunately, similar problems have been addressed by the machine learning community in other fields of study such as microarray analysis and text classification. In this review, we demonstrate that several existing supervised classifiers can be applied effectively to microbiota classification, both for selecting subsets of taxa that are highly discriminative of the type of community, and for building models that can accurately classify unlabeled data. To encourage the development of new approaches to supervised classification of microbiota, we discuss several structures inherent in microbial community data that may be available for exploitation in novel approaches, and we include as supplemental information several benchmark classification tasks for use by the community.

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original_link: https://pubmed.ncbi.nlm.nih.gov/37559119/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10413517/pdf/12916_2023_Article_2975.pdf
site: pubmed
created_at: 2025-09-18 14:11:58.719838
author:
  Li P, Shuai P, Shen S, Zheng H, Sun P, Zhang R, Lan S, Lan Z, Jayawardana T, Yang Y, Zhao J, Liu Y, Chen X, El-Omar EM, Wan Z.

summary:
  Background: The results of human observational studies on the correlation between gut microbiota perturbations and polycystic ovary syndrome (PCOS) have been contradictory. This study aimed to perform the first systematic review and meta-analysis to evaluate the specificity of the gut microbiota in PCOS patients compared to healthy women.

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title:
  Perturbations in gut microbiota composition in patients with polycystic ovary syndrome: a systematic review and meta-analysis.


author: Zhang D, Li J, Xie Y, Wulamu A.
original_link: https://pubmed.ncbi.nlm.nih.gov/37824464/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10569603/pdf/pone.0292582.pdf
site: pubmed
created_at: 2025-09-18 14:11:58.720315
summary:
  Text pre-processing is an important component of a Chinese text classification. At present, however, most of the studies on this topic focus on exploring the influence of preprocessing methods on a few text classification algorithms using English text. In this paper we experimentally compared fifteen commonly used classifiers on two Chinese datasets using three widely used Chinese preprocessing methods that include word segmentation, Chinese specific stop word removal, and Chinese specific symbol removal. We then explored the influence of the preprocessing methods on the final classifications according to various conditions such as classification evaluation, combination style, and classifier selection. Finally, we conducted a battery of various additional experiments, and found that most of the classifiers improved in performance after proper preprocessing was applied. Our general conclusion is that the systematic use of preprocessing methods can have a positive impact on the classification of Chinese short text, using classification evaluation such as macro-F1, combination of preprocessing methods such as word segmentation, Chinese specific stop word and symbol removal, and classifier selection such as machine and deep learning models. We find that the best macro-f1s for categorizing text for the two datasets are 92.13% and 91.99%, which represent improvements of 0.3% and 2%, respectively over the compared baselines.

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title:
  Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification.


author: HaCohen-Kerner Y, Miller D, Yigal Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/32357164/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7194364/pdf/pone.0232525.pdf
site: pubmed
title: The influence of preprocessing on text classification using a bag-of-words representation.
created_at: 2025-09-18 14:11:58.720790
summary:
  Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text preprocessing, e.g., conversion of uppercase letters into lowercase letters, HTML tag removal, stopword removal, punctuation mark removal, lemmatization, correction of common misspelled words, and reduction of replicated characters. We hypothesize that the application of different combinations of preprocessing methods can improve TC results. Therefore, we performed an extensive and systematic set of TC experiments (and this is our main research contribution) to explore the impact of all possible combinations of five/six basic preprocessing methods on four benchmark text corpora (and not samples of them) using three ML methods and training and test sets. The general conclusion (at least for the datasets verified) is that it is always advisable to perform an extensive and systematic variety of preprocessing methods combined with TC experiments because it contributes to improve TC accuracy. For all the tested datasets, there was always at least one combination of basic preprocessing methods that could be recommended to significantly improve the TC using a BOW representation. For three datasets, stopword removal was the only single preprocessing method that enabled a significant improvement compared to the baseline result using a bag of 1,000-word unigrams. For some of the datasets, there was minimal improvement when we removed HTML tags, performed spelling correction or removed punctuation marks, and reduced replicated characters. However, for the fourth dataset, the stopword removal was not beneficial. Instead, the conversion of uppercase letters into lowercase letters was the only single preprocessing method that demonstrated a significant improvement compared to the baseline result. The best result for this dataset was obtained when we performed spelling correction and conversion into lowercase letters. In general, for all the datasets processed, there was always at least one combination of basic preprocessing methods that could be recommended to improve the accuracy results when using a bag-of-words representation.

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author: Huh J, Yetisgen-Yildiz M, Pratt W.
original_link: https://pubmed.ncbi.nlm.nih.gov/24025513/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC3874858/pdf/nihms523972.pdf
site: pubmed
title: Text classification for assisting moderators in online health communities.
created_at: 2025-09-18 14:11:58.721246
summary:
  Objectives: Patients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators' help.

parsed_summary:
  {"background": "\u5728\u7ebf\u5065\u5eb7\u793e\u533a\u4e3a\u60a3\u8005\u63d0\u4f9b\u4e86\u4ea4\u6d41\u4e0e\u4fe1\u606f\u83b7\u53d6\u7684\u5e73\u53f0\uff0c\u4f46\u7531\u4e8e\u8ba8\u8bba\u89c4\u6a21\u5e9e\u5927\uff0c\u7248\u4e3b\u96be\u4ee5\u5168\u7a0b\u53c2\u4e0e\uff0c\u90e8\u5206\u91cd\u8981\u7684\u4ea4\u6d41\u9700\u8981\u7248\u4e3b\u53ca\u65f6\u5e72\u9884\u3002", "contribution": "1. \u5c06\u81ea\u52a8\u6587\u672c\u5206\u7c7b\u5e94\u7528\u4e8e\u5065\u5eb7\u793e\u533a\u7ba1\u7406\uff0c\u663e\u8457\u63d0\u9ad8\u7248\u4e3b\u54cd\u5e94\u6548\u7387\u30022. \u4e3a\u5927\u89c4\u6a21\u5728\u7ebf\u5065\u5eb7\u8bba\u575b\u7b5b\u67e5\u673a\u5236\u63d0\u4f9b\u4e86\u53ef\u884c\u4e14\u7ecf\u6d4e\u7684\u6280\u672f\u65b9\u6848\u3002", "method": "1. \u5e94\u7528\u4f4e\u6210\u672c\u7684\u6587\u672c\u5206\u7c7b\u6280\u672f\uff0c\u5bf9\u5728\u7ebf\u5065\u5eb7\u793e\u533a\u4e2d\u7684\u5e16\u5b50\u8fdb\u884c\u5206\u6790\uff0c\u4ee5\u5224\u65ad\u662f\u5426\u9700\u8981\u7248\u4e3b\u5e2e\u52a9\u3002", "objective": "1. \u63a2\u7d22\u5982\u4f55\u901a\u8fc7\u81ea\u52a8\u6587\u672c\u5206\u7c7b\u68c0\u6d4b\u54ea\u4e9b\u8bba\u575b\u5e16\u5b50\u9700\u8981\u7248\u4e3b\u4ecb\u5165\uff0c\u4ee5\u51cf\u8f7b\u4eba\u5de5\u7b5b\u67e5\u8d1f\u62c5\u5e76\u4fdd\u969c\u793e\u533a\u8d28\u91cf\u3002", "results": "\u4f4e\u6210\u672c\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u80fd\u591f\u533a\u5206\u9700\u7248\u4e3b\u534f\u52a9\u7684\u5e16\u5b50\uff0c\u4ece\u800c\u63d0\u5347\u5e72\u9884\u6548\u7387\u3002"}


author: Wang Y, Li R, Zhou Y, Ling Z, Guo X, Xie L, Liu L.
original_link: https://pubmed.ncbi.nlm.nih.gov/27057545/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC4769744/pdf/BMRI2016-6598307.pdf
site: pubmed
created_at: 2025-09-18 14:11:58.721714
summary:
  Background: Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty.

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title:
  Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification.


author: Koopman B, Karimi S, Nguyen A, McGuire R, Muscatello D, Kemp M, Truran D, Zhang M, Thackway S.
original_link: https://pubmed.ncbi.nlm.nih.gov/26174442/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC4502908/pdf/12911_2015_Article_174.pdf
site: pubmed
title: Automatic classification of diseases from free-text death certificates for real-time surveillance.
created_at: 2025-09-18 14:11:58.722169
summary:
  Background: Death certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV.

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author: 段辛茹;汪青燕;刘彤
date: 2025-04-15
keywords: 柔性电子材料;;健康监测;;互动电子设备;;性能评估;;测量技术;;应用研究;
source: 大众标准化
site: 知网
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-29 18:27:31.060508
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeCVZbTIMJGjbDAmAU6qd5ubkWydeqDVd_-s-NP5eKv9DmyTI02VsZH8Dy8n_b5uhCJobL88fT_YSPps4fp0GtT6aEp06U85dazRG6arg_UePW-iyH98I86-qR1qLqogyNz7d5rvgOR1094b-VkIi6WhpBWEOrqcn18OuJE9y951oQ==&uniplatform=NZKPT&language=CHS

summary:
  随着可穿戴技术的快速发展，柔性电子材料因其在健康监测和互动电子设备中的广泛应用而受到研究者的极大关注。文章综合分析了柔性电子材料的发展历程和主要分类，并探讨了材料性能指标及其影响因素，同时也对现有的先进测量技术进行了总结。针对健康监测领域和互动电子设备，深入剖析了柔性电子材料的实际应用情况，展现了其在提升设备性能与用户体验方面的巨大潜力。此次研究提供了柔性电子材料领域的综述，对促进该领域的创新与发展具有重要意义。

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-29 18:27:31.062615
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1Ggh5Yml3azZtNw%3D%3D

summary:
  随着可穿戴技术的快速发展,柔性电子材料因其在健康监测和互动电子设备中的广泛应用而受到研究者的极大关注.文章综合分析了柔性电子材料的发展历程和主要分类,并探讨了材料性能指标及其影响因素,同时也对现有的先进测量技术进行了总结.针对健康监测领域和互动电子设备,深入剖析了柔性电子材料的实际应用情况,展现了其在提升设备性能与用户体验方面的巨大潜力.此次研究提供了柔性电子材料领域的综述,对促进该领域的创新与发展具有重要意义.

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date: 2024
original_link: https://ieeexplore.ieee.org/document/10524349/
site: ieeeXplore
title: Biodegradable Electronics
created_at: 2025-09-29 18:27:31.063970
summary:
  Summary <p>With the increasing environmental pollution of the electronic wastes, biodegradable electronics have been attracting the attention of global researchers. In this chapter, we focus on biodegradable materials for their applications in biodegradable electrons. Various materials that can be used for biodegradable electrons are summarized, including natural materials and synthetic materials, two&#x2010;dimensional materials, and metals and their oxides. They not only have good mechanical properties and excellent electrical properties but also are biodegradable, biocompatible, and nontoxic materials, which can be completely or partially degraded after deposition to meet the requirements of sustainable development. In addition, biodegradable electronics could not only alleviate the growing e&#x2010;waste problem but also greatly facilitate the development of medical implant devices.</p>

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author: Bin Wu;Linghao Kong;Weiqiu Chen;Davide Riccobelli;Michel Destrade
date: 25 September, 2025
original_link: https://arxiv.org/abs/2509.21488
pdf_url: https://arxiv.org/pdf/2509.21488
site: arxiv
title: Electro-mechanical wrinkling of soft dielectric films bonded to hyperelastic substrates
created_at: 2025-09-29 18:27:31.065218
summary:
  Abstract:Active control of wrinkling in soft film-substrate composites using electric fields is a critical challenge in tunable material systems.

  Here, we investigate the electro-mechanical instability of a soft dielectric film bonded to a hyperelastic substrate, revealing the fundamental mechanisms that enable on-demand surface patterning. For the linearized stability analysis, we use the Stroh formalism and the surface impedance method to obtain exact and sixth-order approximate bifurcation equations that signal the onset of wrinkles.

  We derive the explicit bifurcation equations giving the critical stretch and critical voltage for wrinkling, as well as the corresponding critical wavenumber.

  We look at scenarios where the voltage is kept constant and the stretch changes, and vice versa.

  We provide the thresholds of the shear modulus ratio r_{\rm c}^0 or pre-stretch \lambda_{\rm c}^0 below which the film-substrate system wrinkles mechanically, prior to the application of a voltage.

  These predictions offer theoretical guidance for practical structural design, as the shear modulus ratio r and/or the pre-stretch \lambda can be chosen to be slightly greater than r_{\rm c}^0 and/or \lambda_{\rm c}^0, so that the film-substrate system wrinkles with a small applied voltage.

  Finally, we simulate the full nonlinear behavior using the Finite Element method (FEniCS) to validate our formulas and conduct a post-buckling analysis. This work advances the fundamental understanding of electro-mechanical wrinkling instabilities in soft material systems. By enabling active control of surface morphologies via applied electric fields, our findings open new avenues for adaptive technologies in soft robotics, flexible electronics, smart surfaces, and bioinspired systems.

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author: Hui Z, Zhang L, Ren G, Sun G, Yu HD, Huang W.
original_link: https://pubmed.ncbi.nlm.nih.gov/36763956/
site: pubmed
title: Green Flexible Electronics: Natural Materials, Fabrication, and Applications.
created_at: 2025-09-29 18:27:31.066508
summary:
  The emergence of plastic electronics satisfies the increasing demand for flexible electronics. However, it has caused severe ecological problems. Flexible electronics based on natural materials are increasing to hopefully realize the "green" and eco-friendly concept. Herein, recent advances in the design and fabrication of green flexible electronics are reviewed. First, this review comprehensively introduces various natural materials and derivatives, focusing particularly on fibroin and silk, wood and paper, plants, and biomass. Second, fabrication techniques for modifying natural materials, including physical and chemical methods, are presented, after which their merits and demerits are thoroughly discussed. Green flexible electronics based on natural materials, comprising electrical wires/electrodes, antennas, thermal management devices, transistors, memristors, sensors, energy-harvesting devices, energy-storage devices, displays, actuators, electromagnetic shielding, and integration systems, are described in detail. Finally, perspectives on the existing challenges and opportunities to employ natural materials in green flexible electronics are presented.

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author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-09-29 18:27:31.067720
original_link:
  https://www.cqvip.com/doc/journal/3465772587?sign=663b4fc069cdaf0c9b2533ba5349086da61a860845222f2bb85797f53f2b2cb1&expireTime=1790677609593&resourceId=3465772587

summary:
  可穿戴材料与器件正朝着柔性、轻薄、无感、智能化和可长期佩戴等方向发展,以满足人体生理心理等个性化需求。这一趋势为运动健康监测领域带来了革新,并得到了学术界和工业界的广泛关注。然而,在满足人体个性化发展需求的同时,可穿戴柔性材料与器件本身也面临着机械鲁棒性、信号稳定性、软硬接口连接和生物相容性等性能方面的挑战。因此,本文旨在从实际运动健康监测需求的角度出发,讨论构建可穿戴柔性材料与器件的材料、结构和制备工艺。同时,深入探讨了其在机械、电气和生物性能等方面所面临的主要挑战因素及其解决路径。最后,预测了未来可穿戴柔性电子材料与器件的发展方向,包括全柔性集成、机械鲁棒性的增强、信号解耦与识别的高精度化、监测的稳定性与灵敏度、快速响应性、超薄无感设计、多模态信号处理以及智能化自适应反馈等。

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author: 段辛茹;汪青燕;刘彤
date: 2025-04-15
keywords: 柔性电子材料;;健康监测;;互动电子设备;;性能评估;;测量技术;;应用研究;
source: 大众标准化
site: 知网
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-29 19:45:10.788688
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeAI2zheoYnvaVf58jspsjeDx4nrisft_T0hbFHeWy2h6KEifWnfuHgUr8ErBZWBtH-HK66g1rGJiqdwY5_o8bXj1LvgRaEN_h6apgR77GqmxwA6pbEABuIi0qDHWCg3qUqcWp67KJl1-Uhbw212l6f097s119DoekBlT_sYDnjg4A==&uniplatform=NZKPT&language=CHS

summary:
  随着可穿戴技术的快速发展，柔性电子材料因其在健康监测和互动电子设备中的广泛应用而受到研究者的极大关注。文章综合分析了柔性电子材料的发展历程和主要分类，并探讨了材料性能指标及其影响因素，同时也对现有的先进测量技术进行了总结。针对健康监测领域和互动电子设备，深入剖析了柔性电子材料的实际应用情况，展现了其在提升设备性能与用户体验方面的巨大潜力。此次研究提供了柔性电子材料领域的综述，对促进该领域的创新与发展具有重要意义。

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-29 19:45:10.790865
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1GggybHphdnc0bA%3D%3D

summary:
  随着可穿戴技术的快速发展,柔性电子材料因其在健康监测和互动电子设备中的广泛应用而受到研究者的极大关注.文章综合分析了柔性电子材料的发展历程和主要分类,并探讨了材料性能指标及其影响因素,同时也对现有的先进测量技术进行了总结.针对健康监测领域和互动电子设备,深入剖析了柔性电子材料的实际应用情况,展现了其在提升设备性能与用户体验方面的巨大潜力.此次研究提供了柔性电子材料领域的综述,对促进该领域的创新与发展具有重要意义.

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author: 张旭旭;李家贺;张继朋;李玮;刘雯
date: 2025-09-23 15:00
keywords: EGFR突变;;非小细胞肺癌;;免疫治疗;;靶向治疗;;解读;
source: 中国胸心血管外科临床杂志
site: 知网
title: 2025年美国临床肿瘤学会（ASCO）会议EGFR突变型非小细胞肺癌研究进展解读
created_at: 2025-09-29 19:45:10.792295
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeB1JpU1zk0HSrHpjnn0Zf_idYNKkXVF1uunOPQla8KcB13NCgnKiTXbIhRtaJBLujqH0VrPJQp-nAWeslVJR3zs1DhNdgokQap0vgwhidvMj-LKtjPLL1WNxkgoNyCPLvXpL8kT4pSc_7hPmhWg555JGtby0ikm_MJLj-rsXoR1YA==&uniplatform=NZKPT&language=CHS

summary:
  2025年美国临床肿瘤学会年会在芝加哥召开，年会上关于表皮生长因子受体（epidermal growth factor receptor,EGFR）突变型非小细胞肺癌（non-small cell lung cancer,NSCLC）治疗的研究再次成为焦点。联合治疗策略展现出克服EGFR-酪氨酸激酶抑制剂（TKI）耐药、延长生存期的潜力，同时针对年轻患者和伴有纤维化间质性肺疾病患者的个体化治疗策略也取得进展。然而，耐药机制的复杂性、不同人群的特殊治疗考量，以及社会经济因素对治疗可及性的影响仍是EGFR突变型NSCLC治疗领域面临的挑战。未来需要进一步探索更有效的治疗方案，并扩大精准治疗的可及性，以最大化患者获益。

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author: 朱道林;吴月;王奕力
date: 2025年2期
keywords: 土地管理;土地科学;研究进展;展望
quote: 1
source: 《中国土地科学》
site: 万方
title: 2024年土地科学研究重点进展评述及2025年展望
created_at: 2025-09-29 19:45:10.793590
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISE3pob25nZ3Rka3gyMDI1MDIwMTIaCDl0dzJuOG5s

summary:
  研究目的:梳理比较2024年国内外土地管理领域研究进展,展望未来研究趋势.研究方法:文献分析法.研究结果:2024年国内研究主要关注集体经营性建设用地入市制度优化、耕地保护补偿机制构建、宅基地制度改革、全民所有自然资源资产损害赔偿制度、耕地保护政策、二轮土地承包到期延包政策、存量低效用地再开发政策、宅基地盘活利用、城乡建设用地增减挂钩政策效应以及国土空间用途管制效能等问题.在国际上,主要聚焦于弱势群体土地权益保障、土地分配、住房政策、土地保有权及土地征收等方面.研究结论:2024年土地管理领域研究主要针对实践需求,在土地制度改革、土地政策完善及土地管理行为与效力等方面取得一定进展,土地管理理论探索有待深入.2025年将重点关注土地管理基础理论的深化研究、农村承包地延包过程中的法律问题探讨、耕地占补平衡制度的改革与完善、集体经营性建设用地入市改革与城乡融合发展、盘活存量土地和低效用地再开发支撑高质量发展的体制机制研究等议题.

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date: 2025
original_link: https://ieeexplore.ieee.org/document/11147554/
site: ieeeXplore
created_at: 2025-09-29 19:45:10.797132
author:
  Xin Li;Kun Yuan;Bingchen Li;Fengbin Guan;Yizhen Shao;Zihao Yu;Xijun Wang;Yiting Lu;Wei Luo;Suhang Yao;Ming Sun;Chao Zhou;Zhibo Chen;Radu Timofte;Yabin Zhang;Ao-Xiang Zhang;Tianwu Zhi;Jianzhao Liu;Yang Li;Jingwen Xu;Yiting Liao;Yushen Zuo;Mingyang Wu;Renjie Li;Shengyun Zhong;Zhengzhong Tu;Yufan Liu;Xiangguang Chen;Zuowei Cao;Minhao Tang;Shan Liu;Kexin Zhang;Jingfen Xie;Yan Wang;Kai Chen;Shijie Zhao;Yunchen Zhang;Xiangkai Xu;Hong Gao;Ji Shi;Yiming Bao;Xiugang Dong;Xiangsheng Zhou;Yaofeng Tu;Ying Liang;Yiwen Wang;Xinning Chai;Yuxuan Zhang;Zhengxue Cheng;Yingsheng Qin;Yucai Yang;Rong Xie;Li Song;Wei Sun;Kang Fu;Linhan Cao;Dandan Zhu;Kaiwei Zhang;Yucheng Zhu;Zicheng Zhang;Menghan Hu;Xiongkuo Min;Guangtao Zhai;Zhi Jin;Jiawei Wu;Wei Wang;Wenjian Zhang;Yuhai Lan;Gaoxiong Yi;Hengyuan Na;Wang Luo;Di Wu;Ming Yin Bai;Jiawang Du;Zilong Lu;Zhenyu Jiang;Hui Zeng;Ziguan Cui;Zongliang Gan;Guijin Tang;Xinglin Xie;Kehuan Song;Xiaoqiang Lu;Licheng Jiao;Fang Liu;Xu Liu;Puhua Chen;Ha Thu Nguyen;Katrien De Moor;Seyed Ali Amirshahi;Mohamed-Chaker Larabi;Qi Tang;Linfeng He;Zhiyong Gao;Zixuan Gao;Guohua Zhang;Zhiye Huang;Yi Deng;Qingmiao Jiang;Lu Chen;Yi Yang;Xi Liao;Nourine Mohammed Nadir;Yuxuan Jiang;Qiang Zhu;Siyue Teng;Fan Zhang;Shuyuan Zhu;Bing Zeng;David Bull;Meiqin Liu;Chao Yao;Yao Zhao

summary:
  This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Quality Assessment (KVQ), and (ii) Diffusionbased Image Super-Resolution (KwaiSR). Track 1 aims to advance the development of lightweight and efficient video quality assessment (VQA) models, with an emphasis on eliminating reliance on model ensembles, redundant weights, and other computationally expensive components in the previous IQA/VQA competitions. Track 2 introduces a new short-form UGC dataset tailored for single-image super-resolution, i.e., the KwaiSR dataset. It consists of 1,800 synthetically generated S-UGC image pairs and 1,900 real-world S-UGC images, which are split into training, validation, and test sets using a ratio of 8:1:1. The primary objective of the challenge is to drive research that benefits the user experience of short-form UGC platforms such as Kwai and TikTok. This challenge attracted 266 participants and received 18 valid final submissions with corresponding fact sheets, significantly contributing to the progress of short-form UGC VQA and image superresolution. The project is publicly available at https://github.com/lixinustc/KVQE-Challenge-CVPR-NTIRE2025.

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title:
  Ntire 2025 Challenge on Short-Form Ugc Video Quality Assessment and Enhancement: Methods and Results


author: James Pearson;Hugh Dickinson;Sukanya Sinha;Stephen Serjeant
date: 26 September, 2025
original_link: https://arxiv.org/abs/2509.22609
pdf_url: https://arxiv.org/pdf/2509.22609
site: arxiv
title: Overview of the ESCAPE Dark Matter Test Science Project for Astronomers
created_at: 2025-09-29 19:45:10.798374
summary:
  Abstract:The search for dark matter has been ongoing for decades within both astrophysics and particle physics. Both fields have employed different approaches and conceived a variety of methods for constraining the properties of dark matter, but have done so in relative isolation of one another. From an astronomer's perspective, it can be challenging to interpret the results of dark matter particle physics experiments and how these results apply to astrophysical scales. Over the past few years, the ESCAPE Dark Matter Test Science Project has been developing tools to aid the particle physics community in constraining dark matter properties; however, ESCAPE itself also aims to foster collaborations between research disciplines. This is especially important in the search for dark matter, as while particle physics is concerned with detecting the particles themselves, all of the evidence for its existence lies solely within astrophysics and cosmology. Here, we present a short review of the progress made by the Dark Matter Test Science Project and their applications to existing experiments, with a view towards how this project can foster complementary with astrophysical observations.

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author: Beij A, Verdonk RC, van Santvoort HC, de-Madaria E, Voermans RP.
original_link: https://pubmed.ncbi.nlm.nih.gov/39804691/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC11866315/pdf/UEG2-13-97.pdf
site: pubmed
created_at: 2025-09-29 19:45:10.799574
summary:
  Acute pancreatitis is a common gastrointestinal disease leading to hospitalisation. Recent advancements in its management have primarily focussed on the development of early phase medical interventions targeting inflammatory pathways, optimisation of supportive treatment (including fluid resuscitation, pain management and nutritional management), appropriate use of antibiotics, implementation of minimally invasive interventions for infected necrosis, and the necessity of follow-up for long-term complications. These advancements have significantly improved personalised management and overall outcomes of acute pancreatitis. Despite these efforts, early-phase medical interventions to mitigate disease progression are still lacking and acute pancreatitis remains a heterogeneous disease. Future research and clinical trials are imperative to further optimise current strategies and develop new therapeutic approaches. This review presents an evidence-based approach to the management of acute pancreatitis, highlighting recent developments.

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title:
  Acute Pancreatitis: An Update of Evidence-Based Management and Recent Trends in Treatment Strategies.


author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-09-29 19:45:10.800797
original_link:
  https://www.cqvip.com/doc/journal/3465772587?sign=29e7843a213e84f0f36f7e7d86419f30aabb2388d56c3f968275549ff8205ae4&expireTime=1790682257702&resourceId=3465772587

summary:
  可穿戴材料与器件正朝着柔性、轻薄、无感、智能化和可长期佩戴等方向发展,以满足人体生理心理等个性化需求。这一趋势为运动健康监测领域带来了革新,并得到了学术界和工业界的广泛关注。然而,在满足人体个性化发展需求的同时,可穿戴柔性材料与器件本身也面临着机械鲁棒性、信号稳定性、软硬接口连接和生物相容性等性能方面的挑战。因此,本文旨在从实际运动健康监测需求的角度出发,讨论构建可穿戴柔性材料与器件的材料、结构和制备工艺。同时,深入探讨了其在机械、电气和生物性能等方面所面临的主要挑战因素及其解决路径。最后,预测了未来可穿戴柔性电子材料与器件的发展方向,包括全柔性集成、机械鲁棒性的增强、信号解耦与识别的高精度化、监测的稳定性与灵敏度、快速响应性、超薄无感设计、多模态信号处理以及智能化自适应反馈等。

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author: 李玉强;郑成卓;向宇;牛亚毅;王旭洋;牟晓明
keywords: 草原风沙区;沙地生态系统;植物土壤碳;碳循环;碳固存
source: 中国沙漠
site: 维普
title: 沙地生态系统碳循环:奈曼沙漠化研究站1985—2025年主要研究进展
created_at: 2025-09-29 19:45:10.802031
original_link:
  https://www.cqvip.com/doc/journal/2030726068560399360?sign=69c4d9879c5040230bbfb9ce00877fd44005df1925f83b9828dfefab7232cd73&expireTime=1790682287955&resourceId=2030726068560399360

summary:
  科尔沁沙地是中国北方半干旱草原地带土地沙漠化演变的典型区域,是国家防沙治沙的核心阵地,也是全球变化背景下陆地生态系统碳循环研究的热点区。位于科尔沁沙地西南部的奈曼沙漠化研究站,是中国科学院在北方农牧交错带建立的专门从事土地沙漠化防治的长期定位观测研究平台。本文聚焦植物-土壤系统碳含量/储量动态、土壤呼吸特征、生态系统碳通量变化以及生态修复固碳效应,梳理了该站建站40年来在科尔沁沙地生态系统碳循环研究方面取得的主要进展。研究揭示了沙漠化正逆过程中植被与土壤碳库的变化特征,认知了区域尺度土壤有机碳时空格局及其驱动机制,确定了土壤呼吸对温湿度的响应阈值及其组分在不同生境的贡献比变化,基于长期通量观测明晰了不同类型生态系统碳汇功能年际变异的主控因子,量化了不同生态修复措施的固碳速率与潜力,并阐释了其固碳机理。这些成果对于深化半干旱风沙区生态修复固碳增汇的途径、过程与机理研究,以及为国家“双碳”目标在生态脆弱区的精准实施提供科学支撑与实践指导具有重要意义。

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author: 段辛茹;汪青燕;刘彤
date: 2025-04-15
keywords: 柔性电子材料;;健康监测;;互动电子设备;;性能评估;;测量技术;;应用研究;
source: 大众标准化
site: 知网
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 04:22:31.558937
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeDsFgN81Qm2FNmCrumwkJyOP4WvwxrwUYXBZE8BPD53pMQOXPl2QTawHKLVkLL9PaAqjpJuFTvzUNy7oeOIK6I7xG-jUB9YgXKucuCXvPzNqUS8c3OQMluCq4rOYnJ0q2h36oouiTeK9jWIdyHu8YYkbkt0tbNppJrgOnc6RnUatw==&uniplatform=NZKPT&language=CHS

summary:
  随着可穿戴技术的快速发展，柔性电子材料因其在健康监测和互动电子设备中的广泛应用而受到研究者的极大关注。文章综合分析了柔性电子材料的发展历程和主要分类，并探讨了材料性能指标及其影响因素，同时也对现有的先进测量技术进行了总结。针对健康监测领域和互动电子设备，深入剖析了柔性电子材料的实际应用情况，展现了其在提升设备性能与用户体验方面的巨大潜力。此次研究提供了柔性电子材料领域的综述，对促进该领域的创新与发展具有重要意义。

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 04:22:31.561178
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1GghmbmlhajV1eQ%3D%3D

summary:
  随着可穿戴技术的快速发展,柔性电子材料因其在健康监测和互动电子设备中的广泛应用而受到研究者的极大关注.文章综合分析了柔性电子材料的发展历程和主要分类,并探讨了材料性能指标及其影响因素,同时也对现有的先进测量技术进行了总结.针对健康监测领域和互动电子设备,深入剖析了柔性电子材料的实际应用情况,展现了其在提升设备性能与用户体验方面的巨大潜力.此次研究提供了柔性电子材料领域的综述,对促进该领域的创新与发展具有重要意义.

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author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-09-30 04:22:31.564782
original_link:
  https://www.cqvip.com/doc/journal/3465772587?sign=a86cb4b624a175df858211dbec24fbfddf89dd8cae8be436a8a901b811c7521f&expireTime=1790713313522&resourceId=3465772587

summary:
  可穿戴材料与器件正朝着柔性、轻薄、无感、智能化和可长期佩戴等方向发展,以满足人体生理心理等个性化需求。这一趋势为运动健康监测领域带来了革新,并得到了学术界和工业界的广泛关注。然而,在满足人体个性化发展需求的同时,可穿戴柔性材料与器件本身也面临着机械鲁棒性、信号稳定性、软硬接口连接和生物相容性等性能方面的挑战。因此,本文旨在从实际运动健康监测需求的角度出发,讨论构建可穿戴柔性材料与器件的材料、结构和制备工艺。同时,深入探讨了其在机械、电气和生物性能等方面所面临的主要挑战因素及其解决路径。最后,预测了未来可穿戴柔性电子材料与器件的发展方向,包括全柔性集成、机械鲁棒性的增强、信号解耦与识别的高精度化、监测的稳定性与灵敏度、快速响应性、超薄无感设计、多模态信号处理以及智能化自适应反馈等。

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 05:04:01.377968
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoIeWI5NnZmeXY%3D

summary:
  深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制.大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域.文章总结了 LLM在医疗领域的应用,涵盖了 LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用.同时,进一步讨论了 LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略.最后,文章展望了 LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响.

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author: Hao Yang;Zhaoyong Yang;Ruyang Zhao;Xiaoran Li;Gaoqi Rao
date: 2024
original_link: https://ieeexplore.ieee.org/document/10661162/
site: ieeeXplore
created_at: 2025-09-30 05:04:01.380307
summary:
  In the data analysis process, visualized data can help users gain better insights. To make it easier and faster for users to obtain visual charts from data, natural language interfaces for data visualization have emerged. Users only need to provide the visualization model with the data to be visualized and a description of their visualization needs, and the model will return a visual chart(NL2VIS). In real-world scenarios, most data is stored in relational databases. To visualize this data, it is first necessary to generate a structured query statements based on the user’s visualization requirements(NL2SQL), and then proceed with the subsequent visualization operations. This study breaks down the task of automatic visualization of tabular data in relational databases into three main steps: generating SQL, determining the chart type, and mapping data to visual channels. We utilize the Chain-of-Thought(CoT) technique of generative large language models to address the task of automatic visualization of tabular data. Finally, we evaluated our approach on the nvBench dataset, and the results show that CoT-based automatic visualization of tabular data performs well.

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title:
  The implementation solution for automatic visualization of tabular data in relational databases based on large language models


date: 26 September, 2025
original_link: https://arxiv.org/abs/2509.22653
pdf_url: https://arxiv.org/pdf/2509.22653
site: arxiv
title: See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation
created_at: 2025-09-30 05:04:01.381659
author:
  Chih Yao Hu;Yang-Sen Lin;Yuna Lee;Chih-Hai Su;Jie-Ying Lee;Shr-Ruei Tsai;Chin-Yang Lin;Kuan-Wen Chen;Tsung-Wei Ke;Yu-Lun Liu

summary:
  Abstract:We present See, Point, Fly (SPF), a training-free aerial vision-and-language navigation (AVLN) framework built atop vision-language models (VLMs). SPF is capable of navigating to any goal based on any type of free-form instructions in any kind of environment. In contrast to existing VLM-based approaches that treat action prediction as a text generation task, our key insight is to consider action prediction for AVLN as a 2D spatial grounding task. SPF harnesses VLMs to decompose vague language instructions into iterative annotation of 2D waypoints on the input image. Along with the predicted traveling distance, SPF transforms predicted 2D waypoints into 3D displacement vectors as action commands for UAVs. Moreover, SPF also adaptively adjusts the traveling distance to facilitate more efficient navigation. Notably, SPF performs navigation in a closed-loop control manner, enabling UAVs to follow dynamic targets in dynamic environments. SPF sets a new state of the art in DRL simulation benchmark, outperforming the previous best method by an absolute margin of 63%. In extensive real-world evaluations, SPF outperforms strong baselines by a large margin. We also conduct comprehensive ablation studies to highlight the effectiveness of our design choice. Lastly, SPF shows remarkable generalization to different VLMs. Project page: this https URL

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original_link: https://pubmed.ncbi.nlm.nih.gov/39405325/
site: pubmed
title: Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.
created_at: 2025-09-30 05:04:01.382931
author:
  Bedi S, Liu Y, Orr-Ewing L, Dash D, Koyejo S, Callahan A, Fries JA, Wornow M, Swaminathan A, Lehmann LS, Hong HJ, Kashyap M, Chaurasia AR, Shah NR, Singh K, Tazbaz T, Milstein A, Pfeffer MA, Shah NH.

summary:
  Importance: Large language models (LLMs) can assist in various health care activities, but current evaluation approaches may not adequately identify the most useful application areas.

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author: Seunghan Yang;Juntae Lee;Jihwan Bang;Kyuhong Shim;Minsoo Kim;Simyung Chang
date: 23 September, 2025
original_link: https://arxiv.org/abs/2509.19700
pdf_url: https://arxiv.org/pdf/2509.19700
site: arxiv
title: Learning Contextual Retrieval for Robust Conversational Search
created_at: 2025-09-30 05:04:01.384145
summary:
  Abstract:Effective conversational search demands a deep understanding of user intent across multiple dialogue turns. Users frequently use abbreviations and shift topics in the middle of conversations, posing challenges for conventional retrievers. While query rewriting techniques improve clarity, they often incur significant computational cost due to additional autoregressive steps. Moreover, although LLM-based retrievers demonstrate strong performance, they are not explicitly optimized to track user intent in multi-turn settings, often failing under topic drift or contextual ambiguity. To address these limitations, we propose ContextualRetriever, a novel LLM-based retriever that directly incorporates conversational context into the retrieval process. Our approach introduces: (1) a context-aware embedding mechanism that highlights the current query within the dialogue history; (2) intent-guided supervision based on high-quality rewritten queries; and (3) a training strategy that preserves the generative capabilities of the base LLM. Extensive evaluations across multiple conversational search benchmarks demonstrate that ContextualRetriever significantly outperforms existing methods while incurring no additional inference overhead.

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author: 段辛茹;汪青燕;刘彤
date: 2025-04-15
keywords: 柔性电子材料;;健康监测;;互动电子设备;;性能评估;;测量技术;;应用研究;
source: 大众标准化
site: 知网
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 05:06:20.025234
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeAt2z8v_kto3Cf8UTJinlkR4CNs24lS8NwQvWXtvO5rGnB30IaJEhB5JgGR6ygyUPpSnoCkQWQQ4IoMnkEgLIjtDOh8d9bjqR0oG97Wf3ZaVOQ4Noz9jvdLJgKz0VCNPpfEFL40aoMNz8Xlr8tW9XiI3EI8gHP829UdirfH026d_Q==&uniplatform=NZKPT&language=CHS

summary:
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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 05:06:20.028417
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1Ggh0NHhiY2ZmNA%3D%3D

summary:
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author: 张旭旭;李家贺;张继朋;李玮;刘雯
date: 2025-09-23 15:00
keywords: EGFR突变;;非小细胞肺癌;;免疫治疗;;靶向治疗;;解读;
source: 中国胸心血管外科临床杂志
site: 知网
title: 2025年美国临床肿瘤学会（ASCO）会议EGFR突变型非小细胞肺癌研究进展解读
created_at: 2025-09-30 05:06:20.030369
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeCcAOdvYEpeUkFoKjLUCw523Uguf6b0-jH0c6MKHtPHtq3TsXJ0-NncDSUI4G7PeNMjJX5hGJfyTW2uuoKMwn-TeGTSbia7WCxzUE5G15Y4Qu7URFmy5DjLvLGD5l4FQK4Z6c8myTbHdD-iUWeE6AxvGrYoa77_e280TGnipBo79A==&uniplatform=NZKPT&language=CHS

summary:
  2025年美国临床肿瘤学会年会在芝加哥召开，年会上关于表皮生长因子受体（epidermal growth factor receptor,EGFR）突变型非小细胞肺癌（non-small cell lung cancer,NSCLC）治疗的研究再次成为焦点。联合治疗策略展现出克服EGFR-酪氨酸激酶抑制剂（TKI）耐药、延长生存期的潜力，同时针对年轻患者和伴有纤维化间质性肺疾病患者的个体化治疗策略也取得进展。然而，耐药机制的复杂性、不同人群的特殊治疗考量，以及社会经济因素对治疗可及性的影响仍是EGFR突变型NSCLC治疗领域面临的挑战。未来需要进一步探索更有效的治疗方案，并扩大精准治疗的可及性，以最大化患者获益。

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author: 朱道林;吴月;王奕力
date: 2025年2期
keywords: 土地管理;土地科学;研究进展;展望
quote: 1
source: 《中国土地科学》
site: 万方
title: 2024年土地科学研究重点进展评述及2025年展望
created_at: 2025-09-30 05:06:20.032377
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISE3pob25nZ3Rka3gyMDI1MDIwMTIaCGo0anp1dTZm

summary:
  研究目的:梳理比较2024年国内外土地管理领域研究进展,展望未来研究趋势.研究方法:文献分析法.研究结果:2024年国内研究主要关注集体经营性建设用地入市制度优化、耕地保护补偿机制构建、宅基地制度改革、全民所有自然资源资产损害赔偿制度、耕地保护政策、二轮土地承包到期延包政策、存量低效用地再开发政策、宅基地盘活利用、城乡建设用地增减挂钩政策效应以及国土空间用途管制效能等问题.在国际上,主要聚焦于弱势群体土地权益保障、土地分配、住房政策、土地保有权及土地征收等方面.研究结论:2024年土地管理领域研究主要针对实践需求,在土地制度改革、土地政策完善及土地管理行为与效力等方面取得一定进展,土地管理理论探索有待深入.2025年将重点关注土地管理基础理论的深化研究、农村承包地延包过程中的法律问题探讨、耕地占补平衡制度的改革与完善、集体经营性建设用地入市改革与城乡融合发展、盘活存量土地和低效用地再开发支撑高质量发展的体制机制研究等议题.

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author: 李雯莉;冯小年;钱铁云
date: 2025-09-09 12:47
keywords: 毒性检测;;持续学习;;少样本学习;;对比学习;;大型语言模型;
source: 计算机科学
site: 知网
title: 基于大型语言模型增广的少样本持续毒性检测
created_at: 2025-09-30 05:08:22.876508
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeCvgRFII-2Qyh4oKZkuA9JgrKb3ZEX0OZDhA8zIzx_NJLZ4VKcfbt8dmw_wHanSVjOOYo-4C0L345yWJg_rQi9tO8X4N0bAQrciJ5SQnFXIDLKM_QIzqi7PeilmjxhtPWY7N5Zfv4Pi6nCa3jKtsmxpx7IdGBB9yPQCZ9VV2jO3Fw==&uniplatform=NZKPT&language=CHS

summary:
  毒性言论检测是困扰网络社交媒体的一个具有挑战性的问题。尽管现有方法能够有效识别常见的有毒信息或经由特定扰动模式产生的有毒信息，但也面临两大挑战：（1）由于毒性类型和语言表达的多样性，训练集不可能覆盖所有样本，毒性检测技术面临着毒性文本数据缺乏的问题。（2）现实中的恶意用户倾向于创建新的扰动模式来欺骗文本毒性检测器，如何将模型对于旧扰动模式的检测能力迁移到新扰动模式上，已成为一个亟待解决的问题。为了解决上述问题，该文提出了一种基于大型语言模型增广的少样本持续毒性检测模型。其基本思想是利用大型语言模型对训练集中的样例进行增广，再将持续学习与毒性检测技术相结合，确保毒性检测模型能够持续高效地检测文本中的毒性。此外，该文模型通过上述方式，不仅能够更精确地理解有关不同扰动模式的特征，还能提高其在少样本持续毒性检测任务中的适应性与鲁棒性。该文在最新的DynEscape数据集上进行了实验，结果表明该模型优于现有基线模型，达到了最佳性能。

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 05:08:22.879518
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoIcDQ4NzZsZDg%3D

summary:
  深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制.大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域.文章总结了 LLM在医疗领域的应用,涵盖了 LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用.同时,进一步讨论了 LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略.最后,文章展望了 LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响.

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author: Hongwei Zeng;Zhenjie Hong;Jun Liu;Bifan Wei
date: 2021
original_link: https://ieeexplore.ieee.org/document/9667695/
site: ieeeXplore
title: Multi-task Learning for Multi-turn Dialogue Generation with Topic Drift Modeling
created_at: 2025-09-30 05:08:22.884083
summary:
  Multi-turn dialogue generation aims to generate natural and fluent responses that should be consistent with multiple consecutive utterances history. It is a more challenging task compared to its single-turn counterpart since it requires the model to capture the topic drift along with the multi-turn dialogue history. In this paper, we propose a multi-turn dialogue generation model which incorporates topic drift aware information into a hierarchical encoder-decoder framework to generate coherent responses. This model first utilizes a Convolutional Neural Network (CNN) based topic model to obtain the topic representation of each utterance. Then a topic drift model is employed to encode the sequential topics of multi-turn dialogue history to infer the topic of response. During the response generation, a specially designed topic drift aware generator is proposed to dynamically balance the impact of the inferred topic of response and local word structure. Fur-thermore, we employ multi-task learning to optimize the topic drift model and dialogue generation simultaneously. Extensive experimental results on two benchmark datasets (i.e. Cornell Movie Dialog Corpus and Ubuntu Dialogue Dataset) indicate that our proposed model can generate more coherent responses, and significantly outperform other dialogue generation models.

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author: An Y, Fang Q, Wang L.
original_link: https://pubmed.ncbi.nlm.nih.gov/38157775/
site: pubmed
created_at: 2025-09-30 05:08:22.887318
summary:
  Background: The incidence of cancer is on the rise annually, whereas there exists a significant deficit of healthcare personnel. Inadequate communication between healthcare providers and patients may result in adverse emotional outcomes for the latter and interfere with their treatment progress. A viable solution to alleviate patient distress involves utilizing text generation models as an efficacious tool for delivering patient education.

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title:
  Enhancing patient education in cancer care: Intelligent cancer patient education model for effective communication.


author: 段辛茹;汪青燕;刘彤
date: 2025-04-15
keywords: 柔性电子材料;;健康监测;;互动电子设备;;性能评估;;测量技术;;应用研究;
source: 大众标准化
site: 知网
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 05:09:07.291021
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeCZfL-rMOCfe61jOd-0hNCx48x6PtL4qT-vmpOvZxyxR9exUwiKYxpMPb0MoN5upkiGgYN_fP3Rev4a1TlbiudhjnsJAhpZ-Cx85i5AYZW0dHV1wyY-J5fkwBInBICBmak2Md0IRw7mq68YgJigSQNyzpswvipnMmUWofAuWbcjtA==&uniplatform=NZKPT&language=CHS

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 05:09:07.293112
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1GggzNmZyaHNuZQ%3D%3D

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author: 段辛茹;汪青燕;刘彤
date: 2025-04-15
keywords: 柔性电子材料;;健康监测;;互动电子设备;;性能评估;;测量技术;;应用研究;
source: 大众标准化
site: 知网
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 05:10:49.257817
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=dTSX2bdXfeA3JlD-aV_seyzPxEUZqbmY98sXFRRS9Dp4t8VBKNgd0aYKz-S8_TQQelXNRi82wbLlWSFyVE8G-FHbEkmGaGJGRVB-naK7S-YNKyDWY1undbmXCxacC0wYidK_t5bt7-IBeG8oXFpbArMLa19odPR5XbayRhqq1jEnTcndtrHujA==&uniplatform=NZKPT&language=CHS

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 05:10:49.259562
original_link:
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author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-09-30 05:10:49.262959
original_link:
  https://www.cqvip.com/doc/journal/3465772587?sign=6f94d5bfc8502b45752d340f5259183564eb2953659639a95cb223c06eca064b&expireTime=1790716228128&resourceId=3465772587

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author: 侯冰;杨敏;陈思宇
date: 2025-09-30 11:01
keywords: 人工智能;;政府采购;;决策支持;;数据安全;;智能变革;
source: 实验技术与管理
site: 知网
title: 人工智能+：政府采购领域的智能变革时代
created_at: 2025-09-30 11:57:31.424641
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzRi2At-13fgzpvy3m382EXDOpu4tEClwUGXbF6WP1JsYAVURTyy59r-xnFPa0F1hbn0Ri2FIhe3-brVCzoGizJ84my_rTC3YP62jcFj_6KyqibzUaPuTFUlC3FfPhOXuMalM4E4wg5txs0uIjmrWfsUwD5iVtArWytWchMye8iXeg==&uniplatform=NZKPT&language=CHS

summary:
  近年来，人工智能技术快速发展，按照《新一代人工智能发展规划》及《关于加快场景创新以人工智能高水平应用促进经济高质量发展的指导意见》的要求，各领域积极推进人工智能应用。政府采购作为采购货物、服务和工程等项目的重要手段，涉及大额资金及资源交易，面临多重风险和挑战。人工智能技术通过自动化处理、数据分析和决策支持功能，为提升政府采购效能、确保公平公正公开提供了新路径。该文首先介绍了人工智能发展现状，其次探讨了政府采购与人工智能结合的潜在优势，并研究如何实施这一结合以应对政府采购领域面临的主要挑战。

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author: 朱东云;褚建勋
date: 2024年10期
keywords: 人工智能;知识图谱;研究热点;发展趋势;文献计量;文献述评
quote: 5
source: 《科技管理研究》
site: 万方
title: 中国人工智能研究热点与趋势
created_at: 2025-09-30 11:57:31.427264
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2tqZ2x5ajIwMjQxMDAwMRoIcThoYTM5czg%3D

summary:
  人工智能对伦理、创新、教育和法律带来的影响日益凸显,但传统上关于人工智能的研究多以定性为主、少有数据分析,少数定量研究关注的维度较为单一.为更准确地把握人工智能研究的前沿热点,对发表于中文社会科学引文索引(CSSCI)数据库中 2017-2023 年有关人工智能的文献进行计量分析和系统评述,运用文献分析软件,对文献发表时间、出版期刊、核心作者、发文机构等方面进行可视化分析和知识图谱分析,识别出关于人工智能研究领域的热点.结果发现:中国人工智能的有关研究在2017年进入发展期,2018年进入爆发期,主要从哲学、管理学、教育学和法学的角度开展;人工智能伦理、管理方式变革、教育管理与政策以及人工智能的法律规制成为当前人工智能研究的主要内容;未来人工智能的热点前沿主要包括人工智能与数字经济的结合、人工智能发展的伦理困境、人工智能机器的地位辨析和人工智能对劳动力市场的影响等议题;未来学术界关于人工智能的研究重点会随着技术的进一步迭代和政策的持续深化而变化,研究主题会更为集中且向纵深发展,从识别一般规律到探寻具体对象的可操作化路径,研究路径从理论探索到实践指引演化.

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