# api_taskdetail

author: 谢珺;杨海洋;续欣莹
date: 2025年1期
keywords: 知识图谱补全;多视图注意力机制;交互特征;语义特征
quote: 1
source: 《数据分析与知识发现》
site: 万方
title: 基于多视图融合与多特征提取的知识图谱补全方法
created_at: 2025-09-15 11:08:15.272822
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAxMDA3GghlN2RnNTNiNQ%3D%3D

summary:
  [目的]针对现有知识图谱补全模型知识表示质量低、模型性能差等问题,提出一种基于多视图融合与多特征提取的知识图谱补全方法.[方法]通过视图编码器生成多个单视图网络,利用注意力机制融合不同视图信息作为实体的最终知识表示;通过不同的特征提取器分别提取头实体与关系的语义和交互特征,利用交叉注意力模块融合语义和交互特征并与尾实体进行匹配.[结果]在链接预测任务中的实验结果表明,与基线模型相比,本文模型在通用数据集FB15K-237和WN18RR上的Hits@10指标分别提升0.4和0.7个百分点,在领域数据集Kinship和UMLS上的Hits@10指标分别达到99.0％和99.9％.[局限]在视图更新时未更新关系,关系知识表示向量质量一般.[结论]多视图融合模型能够有效提升知识图谱表示质量,多特征提取框架能够有效提升链接预测精度.

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author: 杨延云;胡军
date: 2025年8期
keywords: 知识图谱;信息抽取;知识融合;知识推理;深度学习
quote: 0
source: 《现代信息科技》
site: 万方
title: 知识图谱构建研究综述
created_at: 2025-09-15 11:08:15.273297
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hkeHhrajIwMjUwODAyMxoIZTdkZzUzYjU%3D

summary:
  知识图谱作为结构化的语义知识库,在信息检索、智能问答和推荐系统等多个领域发挥着关键作用.文章综述了知识图谱构建的三个核心环节:信息抽取、知识融合和知识推理.信息抽取技术从基于规则的方法发展到机器学习模型,再到深度学习模型,目前正朝着减少误差传播和提高准确性的实体关系联合抽取模型方向演进.在知识融合部分,探讨了实体链接和知识合并策略,以及通过实体消歧和实体对齐解决实体识别问题.知识推理部分则分析了基于规则、表示学习和深度学习的推理方法,及其在新知识发现和错误信息纠正中的应用.最后,指出了构建过程中的挑战,并对未来研究方向提出了建议,旨在促进知识图谱研究和应用的发展.

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author: 路稳;吴贞东;彭莉兰
date: 2025年6期
keywords: 知识图谱嵌入;负采样策略;子采样策略;门控机制
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 融合子采样与负采样的自适应知识图谱嵌入模型
created_at: 2025-09-15 11:08:15.273764
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA2MDA5GghlN2RnNTNiNQ%3D%3D

summary:
  [目的]针对知识图谱嵌入模型学习过程中,由于数据规模增长带来的实体采样质量不高、正负样本分布欠均衡等问题,提出一种融合子采样与负采样的自适应知识图谱嵌入模型(SSF).[方法]首先,引入K均值聚类算法,基于实体感知负采样策略,选取与正样本高度相关的负样本,缓解样本稀疏性和采样质量问题;其次,采用多维子采样策略,通过动态调整正负样本比例,优化样本数据集结构,确保样本类别分布均衡;最后,搭建门控网络,通过计算数据集词频,模型能够自适应选择Freq和Uniq采样函数,提升输出实体与关系嵌入的准确性.[结果]在FB15K-237和WNRR18数据集上进行对比实验,SSF模型在MRR指标上相较于基线模型最高提升了 10.7个百分点.[局限]由于负采样策略的计算复杂度较高,未能全面分析SSF模型的复杂度和效率.[结论]SSF模型充分融合了子采样与负采样策略的优点,在MR、MRR、Hit@N三个指标上均优于基线模型,能够提升知识图谱嵌入质量和模型泛化能力.

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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: 2025-09-15 11:08:15.274250
original_link:
  https://www.cqvip.com/doc/journal/3508551509?sign=1ba8e6d9a955e481250eba2e90e8e8ea4a6f97310a2c5e5b7d558eda120598bc&expireTime=1789441310711&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: 王强;吴文武;刘盼
source: 人口与经济
site: 维普
title: 绿色就业研究的知识图谱、热点主题与拓展空间——基于SSCI(2006—2023)的可视化分析
created_at: 2025-09-15 11:08:15.274726
original_link:
  https://www.cqvip.com/doc/journal/3506292255?sign=1b1da896632b6d8f3ec60e063c5af25f4f8ed3e81d76bc062daf9319941c5935&expireTime=1789441310712&resourceId=3506292255

summary:
  在全球应对气候变化和推动低碳转型的背景下,绿色就业逐渐成为经济增长、环境保护和社会包容的关键议题。基于SSCI数据库2006—2023年收录的3989篇绿色就业相关文献,运用CiteSpace软件进行文献计量分析,系统构建了绿色就业领域的知识图谱,识别出包括绿色人力资源管理、绿色供应链和绿色创新在内的核心研究群组、热点主题及其演化路径。研究结果显示,绿色就业领域呈现出强烈的跨学科特征,研究主题从宏观政策逐步拓展到微观企业管理与技术创新层面,涵盖绿色政策与市场协同、技术替代与岗位质量提升等关键议题。基于知识图谱分析,不仅揭示了不同群组间的演化关系及其跨国对比视角,还提出了未来绿色就业研究的拓展空间,包括概念精细化、测量标准构建和技能需求的区域差异化分析,为国内绿色就业研究提供了重要的理论支撑和数据参考,并为我国低碳转型中的绿色就业政策制定提供了国际经验和实践启示。

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author: 谢梦愿;李朝奎;汤仲安;吕强;任伟;唐至娜
source: 地理与地理信息科学
site: 维普
title: 基于知识图谱的建筑信息模型构件语义信息提取
created_at: 2025-09-15 11:08:15.275211
original_link:
  https://www.cqvip.com/doc/journal/3506618902?sign=f94aed03ce7ea2b648f8313afde0646d0e3c2f8fcf1c53b902de3d6dab96be84&expireTime=1789441310712&resourceId=3506618902

summary:
  现有相关工程与技术多关注建筑构件几何信息的映射,构件语义信息挖掘侧重于工程项目需要的单一信息,导致BIM(Building Information Modeling)数据在语义交互和智能分析中的潜力未被充分挖掘。该文提出一种基于知识图谱的BIM建筑构件语义信息提取方法,首先通过设计语义映射规则将IFC(Industry Foundation Classes)模型的实体、属性、物理信息转化为知识图谱,形成可用于语义分析的结构化语义网络,然后采用TransE嵌入模型对构建的BIM知识图谱进行嵌入学习,通过向量化表示增强信息提取能力。以某三层综合建筑楼为研究对象,提取并构建了包含996个BIM语义节点和2173条关系的知识图谱,进一步采用TransE模型进行语义信息嵌入,对提取结果进行验证。实验结果表明:知识图谱能有效提取BIM建筑构件语义信息,选取最优参数后进行TransE模型嵌入学习,实体语义成功率为97.27%,该方法能够精准捕捉建筑构件各类语义信息的关键内容,减少信息遗漏和提取错误,为BIM模型信息分析和决策提供了新思路。

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author: 赵凤娇;王世友
source: 语言教学与研究
site: 维普
title: 基于知识图谱的国际中文数智教材概念模型构建
created_at: 2025-09-15 11:08:15.275676
original_link:
  https://www.cqvip.com/doc/journal/3506649236?sign=7e8ef5c1074629ff744af6f7b8b43ff5581e1bceb139d2df4a65767cf2b0a97c&expireTime=1789441310712&resourceId=3506649236

summary:
  国际中文数智教材是在国际中文数字教材基础上融入智能化技术手段和新教学理念、教材和学材相融合的新形态教材。其最大优势在于打破了纸质教材或数字教材内容间较为单一的结构形态,探索并呈现知识内容之间的多维联系,实现教材内容的重构和自动化生成。针对当前海内外师生对教材选用和使用上的需求,本文提出构建国际中文数智教材的解决方案,并以当前教育知识图谱的技术架构为参照,结合国际中文教材内容的构建逻辑,基于知识图谱技术构建国际中文数智教材概念模型。在此基础上,围绕“个人信息”主题,设计并展示面向初级水平中小学学习者的国际中文数智教材内容,旨在为国际中文教材的数智化转型提供参考和借鉴。

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author: 曹荣荣;柳林;于艳东;王海龙
keywords: 幻觉问题;大语言模型;知识图谱;知识语言协同;可解释性
source: 计算机应用研究
site: 维普
title: 融合知识图谱的大语言模型研究综述
created_at: 2025-09-15 11:08:15.276159
original_link:
  https://www.cqvip.com/doc/journal/3508570122?sign=8cc371cdd2924361b5c2c5f690f296549a614e5e735c1575cddb781fec3c21b3&expireTime=1789441310712&resourceId=3508570122

summary:
  大语言模型在多个垂直领域应用中展现出卓越性能,但其生成内容存在可解释性不足与幻觉问题,严重制约了实际部署。而知识图谱以结构化语义网络形式存储事实知识,为增强大语言模型的可控性与知识约束提供了新路径。为此,系统梳理知识图谱与大语言模型融合的技术路线,从预训练、模型架构改造、微调优化三个阶段分析代表性方法,总结其提升模型可解释性与缓解幻觉的作用机制,并探讨多模态知识表示对齐、动态知识更新滞后等核心挑战。分析表明,知识图谱的深度融合可显著提升大语言模型生成内容的事实一致性,但未来需突破多模态知识对齐、轻量化增量式融合及复杂推理验证等技术瓶颈,推动大语言模型向知识语言协同驱动范式演进,为构建可信、可解释的智能系统提供理论支撑与技术方向。

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author: 陈志;江舟
source: 学校党建与思想教育
site: 维普
title: 知识图谱赋能高校思政课教学的价值、挑战和路径
created_at: 2025-09-15 11:08:15.276628
original_link:
  https://www.cqvip.com/doc/journal/3508618697?sign=438bf3d65a3afae07d485a5f1b624be52d4cba7c146e458d46d5db4176153c43&expireTime=1789441310712&resourceId=3508618697

summary:
  知识图谱通过体系化资源聚合、可视化逻辑呈现、差异化学习引导及智能化辅助教学,为高校思政课教学注入新活力。当前,知识图谱赋能高校思政课教学仍面临理论传递可信度、教学运行稳定度、学生接受融入度、教师掌握熟练度等诸多挑战。鉴于此,应从以下四个方面探索解决路径,即严审知识图谱思政内容强铸权威,提升理论传递公信力;优化知识图谱思政系统兼容适配,夯实教学运行稳定力;强化知识图谱思政课中互动设计,增强学生接受融入力;健全知识图谱应用机制强撑专业,提高教师掌握执行力。

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author: 赖雪冰;李旋;王辩铮;申滨
keywords: 语义通信;联合编解码;知识图谱;文本传输
source: 南京邮电大学学报(自然科学版)
site: 维普
title: 深度联合编解码器融合知识图谱的语义通信性能优化
created_at: 2025-09-15 11:08:15.277082
original_link:
  https://www.cqvip.com/doc/journal/3508542786?sign=4c67cdf2fd4363af75f088e5825e748b86f88b7545e5d394e5da71c7fdecacb8&expireTime=1789441310712&resourceId=3508542786

summary:
  语义通信模型通常采用传统信源信道编解码方案,在低信噪比环境下,文本传输易受噪声影响而导致失真。因此,设计了一种深度联合语义信道编解码器融合知识图谱的语义模型KG-DeepSC,实现文本传输性能优化。该模型在发送端引入冗余三元组过滤模块对知识图谱进行预处理,然后经过基于Transformer的联合编解码器,将三元组作为基本符号传输至接收端,并恢复为自然文本。所设计的编解码器通过学习推理三元组之间的关联,基于其所具有的自注意力机制捕捉并传输三元组语义特征,增强了对抗物理信道噪声的能力,并节约了传输开销。另外,引入基于Flan-T5模型的微调实现自然文本恢复任务,并结合数据增强方法进一步提升模型泛化能力。计算机仿真实验证明,在低信噪比条件下,与传统的Huffman-RS语义编码方法和联合编解码器独立架构DeepSC相比,所提出的KG-DeepSC语义相似度性能分别实现了至少30%和10%的提升,能够更为有效地传输语义知识。

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author: 韦景竹;陈思伶
keywords: 城市记忆;数字记忆;口述档案;知识图谱
source: 图书馆论坛
site: 维普
title: 数字记忆视角下的城市记忆口述档案知识图谱构建——以“顺德·口述历史”项目为例
created_at: 2025-09-15 11:08:15.277561
original_link:
  https://www.cqvip.com/doc/journal/3507499076?sign=0e198eadb2ebcd3ba77e6484ca9c65a73d9159ca4316463936cc216abc964174&expireTime=1789441310712&resourceId=3507499076

summary:
  以知识图谱技术从数据层面拆解、描述、组织、挖掘城市记忆口述档案能丰富中华文化数据库素材,构筑城市记忆链。文章在数字记忆视角下,结合城市记忆口述档案的特点,抽取项目、实物、人物、身份、地点、时间、事件、情感八个核心类搭建本体模型;以“顺德·口述历史”项目为数据源,运用Neo4j图数据库构建顺德记忆知识图谱,从“事件”维度重现顺德社会风貌,从“时空”维度追溯顺德发展轨迹,从“情感”维度挖掘顺德精神底色,实证了记忆图谱在城市记忆传承上的应用价值。

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author: 阎光伟;张云馨;符哲源;焦润海
keywords: 联合抽取;输变电设备;对比学习;知识图谱;边界回归
source: 电工技术学报
site: 维普
title: 基于改进集合预测网络的输变电设备故障知识图谱构建方法
created_at: 2025-09-15 11:08:15.278020
original_link:
  https://www.cqvip.com/doc/journal/3507488505?sign=0e2e4aaafdfb0d4364668a97c23ed55b240d4d37e67e7628cb73ee8e40fd4b2c&expireTime=1789441310712&resourceId=3507488505

summary:
  输变电系统作为电网的重要组成部分,其设备一旦发生故障会造成不可预计的损失。知识图谱通过存储结构化的领域知识,已成为辅助电力领域专业人员进行故障分析与决策的有力工具。实体关系抽取是知识图谱构建中的关键步骤,现有实体关系抽取方法通常忽略了三元组之间的依赖,且存在文本表征能力弱、实体定位模糊及长尾关系分类精确率不高的问题。针对以上问题,该文提出了一种基于改进集合预测网络的实体关系联合抽取模型。该模型基于集合预测网络结构对三元组进行整体建模,首先利用无监督对比学习方式增强输入文本表征,为后续实体关系抽取提供更有效的语义特征;其次利用边界回归算法对实体边界的偏移量进行建模,在网络预测的基础上进一步通过偏移量来修正实体边界,提高实体的识别准确率;最后在关系分类阶段引入代价敏感学习来平衡不同类型三元组的损失,使模型在长尾分布及高错分代价约束下有效地学习长尾关系的特征,降低长尾关系分类的错误率,并且在实际输变电设备故障数据集上进行验证,进而以此构建输变电设备故障知识图谱。在实体关系抽取实验中,精确率、召回率和F1值相较于基线模型分别提升了3.9、5.3、4.6个百分点,实验结果表明,该文所提模型在输变电设备故障数据上能够实现有效的实体关系抽取。此外,该文利用Neo4j图数据库对构建的知识图谱进行存储和可视化,构建的知识图谱能够为后续故障分析和决策提供支持。

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author: 李晓理;刘春芳;耿劭坤
keywords: 知识图谱(KG);大语言模型(LLM);智能教育
source: 计算机工程与应用
site: 维普
title: 知识图谱与大语言模型协同共生模式及其教育应用综述
created_at: 2025-09-15 11:08:15.278472
original_link:
  https://www.cqvip.com/doc/journal/3508556025?sign=e1e99bffea0426c75df9db10dfd01c27db6e277661718138afb9bb84542e7a40&expireTime=1789441310712&resourceId=3508556025

summary:
  近年,人工智能技术,特别是大语言模型、知识图谱技术的迅速发展,为教育的数字化、智能化转型提供了重要的技术条件。分别分析了大语言模型与知识图谱两技术在智能教育领域的应用优势、现状以及存在的问题。在此基础上,深入探讨了知识图谱与大语言模型的协同共生模式,包括两者相互增强的方式方法,并对协同技术研究现状进行了归纳分析,总结了近年来在教育领域的相关应用。最后,对知识图谱与大语言模型技术联合应用于教育领域的发展趋势进行了总结与展望。

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date: 2023
original_link: https://ieeexplore.ieee.org/document/10379452/
site: ieeeXplore
title: IEEE Draft Guide for Application of Knowledge Graphs for Financial Services
created_at: 2025-09-15 11:08:15.278944
summary:
  The application guide of knowledge graphs for financial services is defined in this standard, the knowledge model of knowledge graphs for financial services is proposed, the general framework of technical activities of knowledge graphs for financial services is given, the process and application methods of constructing knowledge graphs for financial services is described, and the practical application factors for use cases of knowledge graphs for financial services is expounded.

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author: Shuang Liang
date: 2023
original_link: https://ieeexplore.ieee.org/document/10184660/
site: ieeeXplore
title: Knowledge Graph Embedding Based on Graph Neural Network
created_at: 2025-09-15 11:08:15.279406
summary:
  The representation of semantic information pertaining to the real world has been active research for some time now. Among the available methods, knowledge graphs have emerged as a widely accepted approach. Meanwhile, graph neural networks (GNNs) have demonstrated excellent performance in embedding graph-based information. Given the natural graph structure of knowledge graphs, employing GNNs to embed them is expected to yield a more interpretable and trustworthy representation of the learned knowledge. In this paper, we propose three customized GNNs for different scenarios of knowledge graph representation, including traditional, multimodal, and uncertain knowledge graphs. In the traditional knowledge graph scenario, we present a graph self-supervised learning method, named deep relation graph infomax (DRGI), which incorporates both the complete graph structure information and semantic information. In the multimodal knowledge graph scenario, we introduce a novel network, named hyper-node relational graph attention network (HRGAT), which combines different modal information with graph structure information for a more precise representation of multimodal knowledge graphs. In the uncertain knowledge graph scenario, we define a novel message-passing paradigm with box embedding, named box graph neural network (BGNN). BGNN leverages both the graph structure information of uncertain knowledge graphs and the probabilistic semantics of box embedding. To validate the effectiveness of our proposed methods, we conduct a series of experiments and report the results. We also discuss possible future work in GNN-based knowledge graph embedding.

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date: 2024
original_link: https://ieeexplore.ieee.org/document/10577610/
site: ieeeXplore
title: IEEE Guide for Application of Knowledge Graphs for Financial Services
created_at: 2025-09-15 11:08:15.279855
summary:
  The application guide of knowledge graphs for financial services is defined in this standard, the knowledge model of knowledge graphs for financial services is proposed, the general framework of technical activities of knowledge graphs for financial services is given, the process and application methods of constructing knowledge graphs for financial services is described, and the practical application factors for use cases of knowledge graphs for financial services is expounded.

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author: Baosheng Xie;Intan Maizura Abd Rashid
date: 2023
original_link: https://ieeexplore.ieee.org/document/10510966/
site: ieeeXplore
created_at: 2025-09-15 11:08:15.280328
summary:
  Computers can process massive and fragmented data information through long-term memory, and classify and correlate them. Therefore, enterprise must build the underlying technology of knowledge graph and cut into the vertical field with the help of computers to manage a large number of knowledge data information at b-end and C-end. Based on the characteristic requirements of the business industry, the rigid demand of enterprise for computer knowledge graph technology is determined. Enterprise have a huge amount of data information, which belongs to non-standardized and fragmented forms such as text, tables and graphics. It is stored in documents. It needs to be sorted into a standardized and related business knowledge graph in order to facilitate the use of relevant staff. In addition, the business documents of enterprise have high requirements for the accuracy and timeliness of data information. Therefore, enterprise need to use computer knowledge graph technology to quickly obtain data information from various channels, sort and classify it with the help of computer, make it structured, and then input it into the corresponding knowledge base. This paper expounds the connotation, main characteristics and basic structure of knowledge graph, analyzes the main risks faced by enterprise' knowledge management, and puts forward some views based on the application of computer knowledge graph technology in enterprise' knowledge management.

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title:
  Research on the Application Mechanism of Enterprise Knowledge Management System Based on Computer Knowledge Graph Technology


author: Fobo Shi;Duantengchuan Li;Xiaoguang Wang;Bing Li;Xindong Wu
date: 2025
original_link: https://ieeexplore.ieee.org/document/10742302/
site: ieeeXplore
title: TGformer: A Graph Transformer Framework for Knowledge Graph Embedding
created_at: 2025-09-15 11:08:15.280778
summary:
  Knowledge graph embedding is efficient method for reasoning over known facts and inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based approaches learn the embedding of missing entities by a single triple only. They ignore the fact that the knowledge graph is essentially a graph structure. Graph-based methods consider graph structure information but ignore the contextual information of nodes in the knowledge graph, making them unable to discern valuable entity (relation) information. In response to the above limitations, we propose a general graph transformer framework for knowledge graph embedding (TGformer). It is the first to use a graph transformer to build knowledge embeddings with triplet-level and graph-level structural features in the static and temporal knowledge graph. Specifically, a context-level subgraph is constructed for each predicted triplet, which models the relation between triplets with the same entity. Afterward, we design a knowledge graph transformer network (KGTN) to fully explore multi-structural features in knowledge graphs, including triplet-level and graph-level, boosting the model to understand entities (relations) in different contexts. Finally, semantic matching is adopted to select the entity with the highest score. Experimental results on several public knowledge graph datasets show that our method can achieve state-of-the-art performance in link prediction.

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author: Jiateng Li
date: 2025
original_link: https://ieeexplore.ieee.org/document/11010624/
site: ieeeXplore
title: Enhancing Cross-Domain Graph Classification with Multi-Teacher Knowledge Distillation
created_at: 2025-09-15 11:08:15.281216
summary:
  Graph classification, as one of the core issues in graph data analysis, demonstrates significant application value in scenarios such as chemical molecular property prediction and protein function analysis. Given that graph data contains both domain-specific feature knowledge and structural knowledge with lower domain dependency, transferring and integrating structural knowledge across heterogeneous graph models from different domains is a viable approach to enhancing graph classification performance in each domain. Motivated by this, we propose a multi-teacher knowledge distillation framework tailored for heterogeneous cross-domain graph data. Under the federated learning setting, this framework effectively utilizes and transfers the structural knowledge embedded in different domain-specific graph models through knowledge distillation. Specifically, the framework employs a knowledge separation model to separate structural knowledge from feature knowledge, where the structural knowledge module takes domain-independent node structural vectors as input, while the feature knowledge module retains local personalized training. Furthermore, the framework incorporates a multi-teacher knowledge distillation mechanism within the federated graph learning setting to fully transfer structural features. Experimental results demonstrate that the proposed framework outperforms existing methods in various cross-domain settings for graph classification.

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date: 2022
original_link: https://ieeexplore.ieee.org/document/9765803/
site: ieeeXplore
title: IEEE Approved Draft Framework of Knowledge Graphs
created_at: 2025-09-15 11:08:15.281699
summary:
  A framework of knowledge graphs is proposed in this standard. The knowledge graph conceptual model, construction and integration process of knowledge graphs, main activities in the processes, and stakeholders of knowledge graphs are described in detail. This standard can be applied in various organizations that plan, design, develop, implement, and apply knowledge and in organizations that develop support technologies, tools, and services to knowledge graphs.

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author: Kaixin Gong;Hui Yao
date: 2023
original_link: https://ieeexplore.ieee.org/document/10288869/
site: ieeeXplore
title: Construction of Knowledge Graph for Special Machining
created_at: 2025-09-15 11:08:15.282150
summary:
  Special machining refers to the general term of processing technology which does not belong to the category of traditional machining technology. The data sources of special machining knowledge are various and rich, including structured, semi-structured and unstructured data sources. According to the characteristics of multi-source heterogeneous knowledge of special machining, this paper uses the powerful semantic machining ability and data organization ability of knowledge graph to solve the problem that the knowledge of special processing is difficult to be mined, so as to create the knowledge graph of special machining and realize the effective fusion and alignment of multi-source heterogeneous data. Therefore, this article uses technologies such as knowledge acquisition, knowledge integration, and network crawlers, and adopts a special processing knowledge diagram from the top - down graph construction model, which realizes the visualization of the Neo4j -based map, making special processing knowledge more intuitive and complete.

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author: Yutong Bu;Yiwei Lu;Yizhi Wang;Bo Huang;Yu Tao;Yongqi Shi
date: 2024
original_link: https://ieeexplore.ieee.org/document/10826769/
site: ieeeXplore
title: A Survey of Temporal Knowledge Graph Reasoning
created_at: 2025-09-15 11:08:15.282592
summary:
  With the rapid development of science and technology, people use communication, sensor, computer technology and other means to collect, process and analyze information. However, in the era of big data, static knowledge graph technology is difficult to effectively integrate massive amounts of information quickly and in real time. Temporal knowledge graph utilizes timestamp information elements to learn and mine knowledge effectively. Temporal knowledge graph reasoning technology can reason and predict future actions or development trends. This article introduces two ways of temporal knowledge graph reasoning technology, namely temporal knowledge graph reasoning for facial completion and temporal knowledge graph reasoning for prediction, and conducts a comparative analysis. In summary, this article aims to enable readers to deeply understand knowledge graph technology and its application in temporal knowledge graph reasoning, providing valuable references for research and application in related fields.

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author: Zibo Yang;Yali Qi;Zixuan Li;Guangyue Wu;Jing Wang
date: 2023
original_link: https://ieeexplore.ieee.org/document/10430349/
site: ieeeXplore
title: Research on the application of Neural Network Model in Knowledge graph completion technology
created_at: 2025-09-15 11:08:15.283094
summary:
  Knowledge Graph (KG) is a technology that employs graph models to represent knowledge and model the interconnected relationships among entities. The purpose of knowledge graph completion techniques is to automatically fill in missing entities, attributes, and relationships within an existing knowledge graph through inference and learning methods. In recent years, neural network models have emerged as powerful learning frameworks and have found extensive applications in the domain of knowledge graph completion. This paper presents a comprehensive investigation and analysis of the application of neural network models in knowledge graph completion techniques. Explored the challenges faced by knowledge graph completion techniques such as sparse data. Point out the future development direction, multimodal fusion and incremental learning knowledge graph completion. The objective is to provide valuable insights and assistance for the advancement of research in knowledge graph completion techniques.

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author: Kausik Hira;Mohd Zaki;Mausam;N. M. Anoop Krishnan
date: 12 September, 2025
original_link: https://arxiv.org/abs/2509.10448
site: arxiv
title: MatSKRAFT: A framework for large-scale materials knowledge extraction from scientific tables
created_at: 2025-09-15 11:08:15.283552
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author: Rui Lu;Zhenyu Hou;Zihan Wang;Hanchen Zhang;Xiao Liu;Yujiang Li;Shi Feng;Jie Tang;Yuxiao Dong
date: 12 September, 2025
original_link: https://arxiv.org/abs/2509.10446
pdf_url: https://arxiv.org/pdf/2509.10446
site: arxiv
title: DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
created_at: 2025-09-15 11:08:15.283993
summary:
  Abstract:Augmenting large language models (LLMs) with browsing tools substantially improves their potential as deep search agents to solve complex, real-world tasks. Yet, open LLMs still perform poorly in such settings due to limited long-horizon reasoning capacity with browsing tools and the lack of sufficiently difficult supervised data. To address these challenges, we present DeepDive to advance deep search agents. First, we propose a strategy to automatically synthesize complex, difficult, and hard-to-find questions from open knowledge graphs. Second, we apply end-to-end multi-turn reinforcement learning (RL) to enhance LLMs' long-horizon reasoning with deep search. Experiments show that DeepDive-32B achieves a new open-source competitive result on BrowseComp, outperforming WebSailor, DeepSeek-R1-Browse, and Search-o1. We demonstrate that multi-turn RL training improves deep search ability and significantly contributes to the performance improvements across multiple benchmarks. We observe that DeepDive enables test-time scaling of tool calls and parallel sampling. All datasets, models, and code are publicly available at this https URL.

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author: Ze Fu;Pinhao Song;Yutong Hu;Renaud Detry
date: 12 September, 2025
original_link: https://arxiv.org/abs/2509.10416
pdf_url: https://arxiv.org/pdf/2509.10416
site: arxiv
title: TASC: Task-Aware Shared Control for Teleoperated Manipulation
created_at: 2025-09-15 11:08:15.284464
summary:
  Abstract:We present TASC, a Task-Aware Shared Control framework for teleoperated manipulation that infers task-level user intent and provides assistance throughout the task. To support everyday tasks without predefined knowledge, TASC constructs an open-vocabulary interaction graph from visual input to represent functional object relationships, and infers user intent accordingly. A shared control policy then provides rotation assistance during both grasping and object interaction, guided by spatial constraints predicted by a vision-language model. Our method addresses two key challenges in general-purpose, long-horizon shared control: (1) understanding and inferring task-level user intent, and (2) generalizing assistance across diverse objects and tasks. Experiments in both simulation and the real world demonstrate that TASC improves task efficiency and reduces user input effort compared to prior methods. To the best of our knowledge, this is the first shared control framework that supports everyday manipulation tasks with zero-shot generalization. The code that supports our experiments is publicly available at this https URL.

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author: Yifan Jia;Ye Tian;Yanbin Wang;Jianguo Sun;Haitao Xu
date: 12 September, 2025
original_link: https://arxiv.org/abs/2509.10252
pdf_url: https://arxiv.org/pdf/2509.10252
site: arxiv
title: ExDoS: Expert-Guided Dual-Focus Cross-Modal Distillation for Smart Contract Vulnerability Detection
created_at: 2025-09-15 11:08:15.284916
summary:
  Abstract:The success of smart contracts has made them a target for attacks, but their closed-source nature often forces vulnerability detection to work on bytecode, which is inherently more challenging than source-code-based analysis. While recent studies try to align source and bytecode embeddings during training to transfer knowledge, current methods rely on graph-level alignment that obscures fine-grained structural and semantic correlations between the two modalities. Moreover, the absence of precise vulnerability patterns and granular annotations in bytecode leads to depriving the model of crucial supervisory signals for learning discriminant features. We propose ExDoS to transfer rich semantic knowledge from source code to bytecode, effectively supplementing the source code prior in practical settings. Specifically, we construct semantic graphs from source code and control-flow graphs from bytecode. To address obscured local signals in graph-level contract embeddings, we propose a Dual-Attention Graph Network introducing a novel node attention aggregation module to enhance local pattern capture in graph embeddings. Furthermore, by summarizing existing source code vulnerability patterns and designing a corresponding set of bytecode-level patterns for each, we construct the first dataset of vulnerability pattern annotations aligned with source code definitions to facilitate fine-grained cross-modal alignment and the capture of function-level vulnerability signals. Finally, we propose a dual-focus objective for our cross-modal distillation framework, comprising: a Global Semantic Distillation Loss for transferring graph-level knowledge and a Local Semantic Distillation Loss enabling expert-guided, fine-grained vulnerability-specific distillation. Experiments on real-world contracts demonstrate that our method achieves consistent F1-score improvements (3\%--6\%) over strong baselines.

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author: Mustapha Adamu;Qi Zhang;Huitong Pan;Longin Jan Latecki;Eduard C. Dragut
date: 12 September, 2025
original_link: https://arxiv.org/abs/2509.10087
pdf_url: https://arxiv.org/pdf/2509.10087
site: arxiv
title: Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
created_at: 2025-09-15 11:08:15.285371
summary:
  Abstract:The growing complexity and volume of climate science literature make it increasingly difficult for researchers to find relevant information across models, datasets, regions, and variables. This paper introduces a domain-specific Knowledge Graph (KG) built from climate publications and broader scientific texts, aimed at improving how climate knowledge is accessed and used. Unlike keyword based search, our KG supports structured, semantic queries that help researchers discover precise connections such as which models have been validated in specific regions or which datasets are commonly used with certain teleconnection patterns. We demonstrate how the KG answers such questions using Cypher queries, and outline its integration with large language models in RAG systems to improve transparency and reliability in climate-related question answering. This work moves beyond KG construction to show its real world value for climate researchers, model developers, and others who rely on accurate, contextual scientific information.

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author: Hailong Yang;Mingxian Gu;Jianqi Wang;Guanjin Wang;Zhaohong Deng
date: 12 September, 2025
original_link: https://arxiv.org/abs/2509.10054
pdf_url: https://arxiv.org/pdf/2509.10054
site: arxiv
created_at: 2025-09-15 11:08:15.285844
summary:
  Abstract:The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of Multi-Agent Systems (MAS) in supporting humans with complex, real-world tasks. However, MAS still face challenges in effective task planning when handling highly complex tasks with uncertainty, often resulting in misleading or incorrect outputs that hinder task execution. To address this, we propose XAgents, a unified multi-agent cooperative framework built on a multipolar task processing graph and IF-THEN rules. XAgents uses the multipolar task processing graph to enable dynamic task planning and handle task uncertainty. During subtask processing, it integrates domain-specific IF-THEN rules to constrain agent behaviors, while global rules enhance inter-agent collaboration. We evaluate the performance of XAgents across three distinct datasets, demonstrating that it consistently surpasses state-of-the-art single-agent and multi-agent approaches in both knowledge-typed and logic-typed question-answering tasks. The codes for XAgents are available at: this https URL.

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title:
  XAgents: A Unified Framework for Multi-Agent Cooperation via IF-THEN Rules and Multipolar Task Processing Graph


author: Chun-Hung Liu;Youngho Yoo
date: 11 September, 2025
original_link: https://arxiv.org/abs/2509.09895
pdf_url: https://arxiv.org/pdf/2509.09895
site: arxiv
title: Tree-width of a graph excluding an apex-forest or a wheel as a minor
created_at: 2025-09-15 11:08:15.286301
summary:
  Abstract:The Grid Minor Theorem states that for every planar graph H, there exists a smallest integer f(H) such that every graph with tree-width at least f(H) contains H as a minor. The only known lower bounds on f(H) beyond the trivial bound f(H)\geq |V(H)|-1 come from the maximum number of disjoint cycles in H. In this paper, we study f(H) for planar graphs H with no two disjoint cycles. We prove that f(H)=|V(H)|-1 for every apex-forest H. This result improves a bound of Leaf and Seymour and contains all known large graphs H meeting the trivial lower bound to our knowledge. We also prove that f(H)\leq \max\{\tfrac32|V(H)|-\tfrac92,|V(H)|-1\} for every wheel H.

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author: Mingyang Li;Viktor Schlegel;Tingting Mu;Warren Del-Pinto;Goran Nenadic
date: 4 September, 2025
original_link: https://arxiv.org/abs/2509.09699
pdf_url: https://arxiv.org/pdf/2509.09699
site: arxiv
title: Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs
created_at: 2025-09-15 11:08:15.286748
summary:
  Abstract:Mapping clinical documents to standardised clinical vocabularies is an important task, as it provides structured data for information retrieval and analysis, which is essential to clinical research, hospital administration and improving patient care. However, manual coding is both difficult and time-consuming, making it impractical at scale. Automated coding can potentially alleviate this burden, improving the availability and accuracy of structured clinical data. The task is difficult to automate, as it requires mapping to high-dimensional and long-tailed target spaces, such as the International Classification of Diseases (ICD). While external knowledge sources have been readily utilised to enhance output code representation, the use of external resources for representing the input documents has been underexplored. In this work, we compute a structured representation of the input documents, making use of document-level knowledge graphs (KGs) that provide a comprehensive structured view of a patient's condition. The resulting knowledge graph efficiently represents the patient-centred input documents with 23\% of the original text while retaining 90\% of the information. We assess the effectiveness of this graph for automated ICD-9 coding by integrating it into the state-of-the-art ICD coding architecture PLM-ICD. Our experiments yield improved Macro-F1 scores by up to 3.20\% on popular benchmarks, while improving training efficiency. We attribute this improvement to different types of entities and relationships in the KG, and demonstrate the improved explainability potential of the approach over the text-only baseline.

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author: Hermann Kroll;Pascal Sackhoff;Bill Matthias Thang;Christin Katharina Kreutz;Wolf-Tilo Balke
date: 18 August, 2025
original_link: https://arxiv.org/abs/2509.09687
pdf_url: https://arxiv.org/pdf/2509.09687
site: arxiv
title: Demonstrating Narrative Pattern Discovery from Biomedical Literature
created_at: 2025-09-15 11:08:15.287228
summary:
  Abstract:Digital libraries maintain extensive collections of knowledge and need to provide effective access paths for their users. For instance, PubPharm, the specialized information service for Pharmacy in Germany, provides and develops access paths to their underlying biomedical document collection. In brief, PubPharm supports traditional keyword-based search, search for chemical structures, as well as novel graph-based discovery workflows, e.g., listing or searching for interactions between different pharmaceutical entities. This paper introduces a new search functionality, called narrative pattern mining, allowing users to explore context-relevant entities and entity interactions. We performed interviews with five domain experts to verify the usefulness of our prototype.

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author: Yikuan Xia;Jiazun Chen;Yirui Zhan;Suifeng Zhao;Weipeng Jiang;Chaorui Zhang;Wei Han;Bo Bai;Jun Gao
date: 12 August, 2025
original_link: https://arxiv.org/abs/2509.09681
pdf_url: https://arxiv.org/pdf/2509.09681
site: arxiv
title: DB3 Team's Solution For Meta KDD Cup' 25
created_at: 2025-09-15 11:08:15.287690
summary:
  Abstract:This paper presents the db3 team's winning solution for the Meta CRAG-MM Challenge 2025 at KDD Cup'25. Addressing the challenge's unique multi-modal, multi-turn question answering benchmark (CRAG-MM), we developed a comprehensive framework that integrates tailored retrieval pipelines for different tasks with a unified LLM-tuning approach for hallucination control. Our solution features (1) domain-specific retrieval pipelines handling image-indexed knowledge graphs, web sources, and multi-turn conversations; and (2) advanced refusal training using SFT, DPO, and RL. The system achieved 2nd place in Task 1, 2nd place in Task 2, and 1st place in Task 3, securing the grand prize for excellence in ego-centric queries through superior handling of first-person perspective challenges.

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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: 2025-09-15 11:08:15.288140
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: 2025-09-15 11:08:15.288620
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: Zeng X, Tu X, Liu Y, Fu X, Su Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/34649044/
site: pubmed
title: Toward better drug discovery with knowledge graph.
created_at: 2025-09-15 11:08:15.289078
summary:
  Drug discovery is the process of new drug identification. This process is driven by the increasing data from existing chemical libraries and data banks. The knowledge graph is introduced to the domain of drug discovery for imposing an explicit structure to integrate heterogeneous biomedical data. The graph can provide structured relations among multiple entities and unstructured semantic relations associated with entities. In this review, we summarize knowledge graph-based works that implement drug repurposing and adverse drug reaction prediction for drug discovery. As knowledge representation learning is a common way to explore knowledge graphs for prediction problems, we introduce several representative embedding models to provide a comprehensive understanding of knowledge representation learning.

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author: Huang Q, Li G.
original_link: https://pubmed.ncbi.nlm.nih.gov/39244959/
site: pubmed
title: Knowledge graph based reasoning in medical image analysis: A scoping review.
created_at: 2025-09-15 11:08:15.289521
summary:
  Automated computer-aided diagnosis (CAD) is becoming more significant in the field of medicine due to advancements in computer hardware performance and the progress of artificial intelligence. The knowledge graph is a structure for visually representing knowledge facts. In the last decade, a large body of work based on knowledge graphs has effectively improved the organization and interpretability of large-scale complex knowledge. Introducing knowledge graph inference into CAD is a research direction with significant potential. In this review, we briefly review the basic principles and application methods of knowledge graphs firstly. Then, we systematically organize and analyze the research and application of knowledge graphs in medical imaging-assisted diagnosis. We also summarize the shortcomings of the current research, such as medical data barriers and deficiencies, low utilization of multimodal information, and weak interpretability. Finally, we propose future research directions with possibilities and potentials to address the shortcomings of current approaches.

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author: Rajabi E, Kafaie S.
original_link: https://pubmed.ncbi.nlm.nih.gov/37203473/
site: pubmed
title: Building a Disease Knowledge Graph.
created_at: 2025-09-15 11:08:15.289968
summary:
  Knowledge graphs have proven themselves as a robust tool in clinical applications to aid patient care and help identify treatments for new diseases. They have impacted many information retrieval systems in healthcare. In this study, we construct a disease knowledge graph using Neo4j (a knowledge graph tool) for a disease database to answer complex questions that are time-consuming and labour-intensive to be answered in the previous system. We demonstrate that new information can be inferred in a knowledge graph based on existing semantic relationships between the medical concepts and the ability to perform reasoning in the knowledge graph.

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author: Liu T, Shen H, Chang L, Li L, Li J.
original_link: https://pubmed.ncbi.nlm.nih.gov/37117327/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10147700/pdf/41598_2023_Article_33984.pdf
site: pubmed
title: Iterative heterogeneous graph learning for knowledge graph-based recommendation.
created_at: 2025-09-15 11:08:15.290432
summary:
  Incorporating knowledge graphs into recommendation systems has attracted wide attention in various fields recently. A Knowledge graph contains abundant information with multi-type relations among multi-type nodes. The heterogeneous structure reveals not only the connectivity but also the complementarity between the nodes within a KG, which helps to capture the signal of potential interest of the user. However, existing research works have limited abilities in dealing with the heterogeneous nature of knowledge graphs, resulting in suboptimal recommendation results. In this paper, we propose a new recommendation method based on iterative heterogeneous graph learning on knowledge graphs (HGKR). By treating a knowledge graph as a heterogeneous graph, HGKR achieves more fine-grained modeling of knowledge graphs for recommendation. Specifically, we incorporate the graph neural networks into the message passing and aggregating of entities within a knowledge graph both at the graph and the semantic level. Furthermore, we designed a knowledge-perceiving item filter based on an attention mechanism to capture the user's potential interest in their historical preferences for the enhancement of recommendation. Extensive experiments conducted on two datasets in the context of two recommendations reveal the excellence of our proposed method, which outperforms other benchmark models.

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author: Varshney D, Zafar A, Behera NK, Ekbal A.
original_link: https://pubmed.ncbi.nlm.nih.gov/37100505/
site: pubmed
title: Knowledge graph assisted end-to-end medical dialog generation.
created_at: 2025-09-15 11:08:15.290887
summary:
  Medical dialog systems have the potential to assist e-medicine in improving access to healthcare services, improving patient treatment quality, and lowering medical expenses. In this research, we describe a knowledge-grounded conversation generation model that demonstrates how large-scale medical information in the form of knowledge graphs can aid in language comprehension and generation in medical dialog systems. Generic responses are often produced by existing generative dialog systems, resulting in monotonous and uninteresting conversations. To solve this problem, we combine various pre-trained language models with a medical knowledge base (UMLS) to generate clinically correct and human-like medical conversations using the recently released MedDialog-EN dataset. The medical-specific knowledge graph contains broadly 3 types of medical-related information, including disease, symptom and laboratory test. We perform reasoning over the retrieved knowledge graph by reading the triples in each graph using MedFact attention, which allows us to use semantic information from the graphs for better response generation. In order to preserve medical information, we employ a policy network, which effectively injects relevant entities associated with each dialog into the response. We also study how transfer learning can significantly improve the performance by utilizing a relatively small corpus, created by extending the recently released CovidDialog dataset, containing the dialogs for diseases that are symptoms of Covid-19. Empirical results on the MedDialog corpus and the extended CovidDialog dataset demonstrate that our proposed model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation and human judgment.

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author: Boudin M, Diallo G, Drancé M, Mougin F.
original_link: https://pubmed.ncbi.nlm.nih.gov/38057380/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10700660/pdf/41597_2023_Article_2757.pdf
site: pubmed
title: The OREGANO knowledge graph for computational drug repurposing.
created_at: 2025-09-15 11:08:15.291335
summary:
  Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).

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author: Li J, Gao J, Feng B, Jing Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/36412326/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10161524/pdf/baac100.pdf
site: pubmed
title: PlagueKD: a knowledge graph-based plague knowledge database.
created_at: 2025-09-15 11:08:15.291816
summary:
  Plague has been confirmed as an extremely horrific international quarantine infectious disease attributed to Yersinia pestis. It has an extraordinarily high lethal rate that poses a serious hazard to human and animal lives. With the deepening of research, there has been a considerable amount of literature related to the plague that has never been systematically integrated. Indeed, it makes researchers time-consuming and laborious when they conduct some investigation. Accordingly, integrating and excavating plague-related knowledge from considerable literature takes on a critical significance. Moreover, a comprehensive plague knowledge base should be urgently built. To solve the above issues, the plague knowledge base is built for the first time. A database is built from the literature mining based on knowledge graph, which is capable of storing, retrieving, managing and accessing data. First, 5388 plague-related abstracts that were obtained automatically from PubMed are integrated, and plague entity dictionary and ontology knowledge base are constructed by using text mining technology. Second, the scattered plague-related knowledge is correlated through knowledge graph technology. A multifactor correlation knowledge graph centered on plague is formed, which contains 9633 nodes of 33 types (e.g. disease, gene, protein, species, symptom, treatment and geographic location), as well as 9466 association relations (e.g. disease-gene, gene-protein and disease-species). The Neo4j graph database is adopted to store and manage the relational data in the form of triple. Lastly, a plague knowledge base is built, which can successfully manage and visualize a large amount of structured plague-related data. This knowledge base almost provides an integrated and comprehensive plague-related knowledge. It should not only help researchers to better understand the complex pathogenesis and potential therapeutic approaches of plague but also take on a key significance to reference for exploring potential action mechanisms of corresponding drug candidates and the development of vaccine in the future. Furthermore, it is of great significance to promote the field of plague research. Researchers are enabled to acquire data more easily for more effective research. Database URL: http://39.104.28.169:18095/.

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author: Lan Y, He S, Liu K, Zeng X, Liu S, Zhao J.
original_link: https://pubmed.ncbi.nlm.nih.gov/34844576/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8628388/pdf/12911_2021_Article_1622.pdf
site: pubmed
created_at: 2025-09-15 11:08:15.292271
summary:
  Background: Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness.

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title:
  Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion.


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: 2025-09-16 13:05:29.284124
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYEnnVMWT7D-slEstymlMwpFHaL71GnytgJEgACw3iOLI2m2Kf10VEyPf1YGMEmVNm9n3hdeZ9aW30oZyuwaa9OiGQp2Uv4e5VGhsHkrGw9iau3FitjCZt4x-9JPM6YB4eiUN2swBRjrs7HpDPy8iiqiVAH01tkGjzgHGWX3oGrmxA==&uniplatform=NZKPT&language=CHS

summary:
  传统文本分类模型存在分类精度有限、文本信息损失值高的不足。基于此，借鉴PVT模型的设计理念，通过缩放因子对查询向量实施同步缩放，实现对其核心注意力模块的改进，并结合BERT模型，提出BERT-sPTT文本分类模型。在THUNews数据集、复旦中文数据集、AG新闻数据集进行验证，实验结果显示三个数据集上该模型ACC值与F1值优于其他对比模型。该模型主要优势在于结合BERT捕捉文本深层语义信息方面的能力和PVT金字塔结构在特征提取和下采样方面的高效性。

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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: 2025-09-16 13:05:29.284840
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYEnnVMWT7D-slEstymlMwpFo_jLPY_nBeevB1i77VG3ukPKpYvCkVdOeAtCz2xJq-ZZMzXmJ54-70tIfoaQ4i__WPieXpOayJ1x2YYJEJB4wsglyzjAqJjhQxRXpmi50JD_bZN87Fm2Cko0lJoYuWWywmW_JRKM1pG7jWoJdY_APQ==&uniplatform=NZKPT&language=CHS

summary:
  以物理实验课后练习题文字回答的自动评分为例，探讨了基于中文文本分类技术在大学物理实验教学评价中的应用.依据SOLO分类理论制定了大学物理实验课后练习题文字回答的分类规则，在比较不同分类器性能指标后选择出最优分类器，实现对学生文字回答的自动分类和评价.结果表明，利用中文文本分类技术可实现学生物理实验课后练习题的文本自动分类，能够帮助教师对学生进行高效评价与分类统计，为教师教学提供有效的改进依据.

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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: 2025-09-16 13:05:29.285342
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYF9wPCse_TJ9qsxXCNMAZpwZ-pvwT750r-9xT62N4NrXZ1nlaDmbELnzljUOS4jXlL0gn29G3ZNYhx95iCWCmqk7QPhHe2WSZqI0dskTpIQMHrXLsFod_8d-7WcXztkc1X_n631gav7WRCl-iOooo6BOavWao7gsnpSPgmM3ff6Fg==&uniplatform=NZKPT&language=CHS

summary:
  随着互联网和通信技术的不断发展,社交媒体平台每天生成着大量的文本数据,这些数据不仅包含丰富的情感表达,还涉及多种文本类别。如何准确地进行情感分类以及对文本主题多分类,对社会生活具有重要的现实意义和价值。文本分类作为自然语言处理领域的重要内容,分类模型的性能一直深受该领域学者的研究。基于词典和规则的文本分类方法,可以通过预定义的词汇表或规则来直接判断文本的情感倾向或主题类别。基于机器学习的文本分类方法,能够通过学习标注文本中的语义特征,进而构建分类器对目标文本进行预测。基于深度学习的文本分类方法,利用多层非线性变换能够从文本数据中学习并提取到更深层次的语义特征,从而更准确地对文本类别或情感倾向进行分类。目前文本分类面临着更加复杂且细粒度化的情感表达,以及更为多样化的文本类别,为了应对这些挑战,本文提出了三种基于动态语义建模和特征提取的文本分类方法。具体工作内容如下: (1)针对句子级情感分类过程中句子独立于文档存在导致缺乏完整上下文语义的问题,提出一种通过权重分配策略增强局部情感特征的方法进行句子级情感分类。该方法先使用动态预训练模型对文本进行向量化表示,得到文本序列的动态词向量矩阵。再利...

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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: 2025-09-16 13:05:29.285877
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYF9wPCse_TJ9qsxXCNMAZpwZ-pvwT750r-9xT62N4NrXUsU6nUQG0d_JLOC0LJnLU0Y2xjXTS5U_UionoQLbgTthqNdTSojp2ijAFjDYYZAw_MmzofSU6DuHGYYqT0ifQpeX8zWB35HmvKffKByNfSdNVRuETCzM0EYqC19B2Kccx7_fEwkXJE0&uniplatform=NZKPT&language=CHS

summary:
  文本分类作为自然语言处理领域的核心任务,其主要目标在于通过语义理解和信息归纳来优化用户的信息管理效率。在文本分类预测任务中,图卷积网络将输入文本构建为词-文档图结构,极大地提高了分类结果的精度,因此该模型被广泛应用于该领域。然而,其现有方法仅依赖词-文档图中的文档节点对文档类别进行预测,忽视了词节点在分类任务中的潜在辅助价值,存在语义利用不足的问题。此外,传统方法的词-文档图节点仅以独热编码作为初始化,存在特征表达能力不足以及文本语义提取不够深层的问题。针对以上问题,本文提出了两种基于图卷积网络的文本分类模型,研究了图卷积网络方法在文本分类中的应用,以进一步提高文本分类任务的准确率。论文的主要研究工作如下: (1)针对目前基于图卷积网络的文本分类方法对语义信息利用不够充分的问题,本文以经典的Text GCN为基础进行优化改进。首先,为了减小梯度消失问题并增强信息传递,本文采用Mish函数替换Text GCN中的Re LU激活函数。Mish函数具有平滑的梯度曲线,能够在深层网络中更好地传递梯度,从而提升模型的训练效果。其次,本文引入了词性标注和命名实体识别作为文本分类的辅助任务,结合多任务...

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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: 2025-09-16 13:05:29.286754
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYF9wPCse_TJ9qsxXCNMAZpwZ-pvwT750r-9xT62N4NrXfCwGuhD1QNFFDrPTK-uWh_8XlqvdkxO-q63nefYhwXr0iLXlRicn-IufSD0erCUQ0Xo0jHkJjqu8Nn83I_1BfkXs_JxOoHKcTDbuWFETcG6xRtcEFgxsiLYFyZvm2peew==&uniplatform=NZKPT&language=CHS

summary:
  近年来,随着互联网新闻数据量的指数级增长和传播渠道的碎片化趋势,新闻文本分类技术在信息管理、个性化推荐及舆情监测等领域的重要性日益凸显。然而,现有方法在单标签新闻文本分类中面临上下文信息提取困难、语义特征模糊等问题,在层次标签分类中则存在标签层次信息利用不足、文本与结构交互效率低下的挑战。为克服这些局限,本文针对新闻文本分类的两种模式,分别提出了基于特征融合的单标签分类模型CBDN和基于动态层次标签融合的层次分类模型DHFPE,并通过系统实验验证了其性能与应用价值。 在单标签分类任务中,CBDN模型使用经过全词掩码策略改进的BERT-wwm做为嵌入层,通过全词掩码方法优化中文新闻文本的语义表示,生成高质量的词向量;引入长链接双向长短期记忆网络和多头注意力机制,分别捕捉时序依赖和多视角语义关联;采用改进的金字塔卷积神经网络提取多尺度局部特征,并通过加权融合与门控机制实现全局、时序及局部特征的动态交互。在层次分类任务中,DHFPE模型以Ro BERTa为基础,通过提示嵌入机制将层次标签路径融入文本输入,利用动态层次特征融合生成更具区分度的表示;引入基于注意力机制的对比学习策略,通过生成增强正...

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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: 2025-09-16 13:05:29.287376
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYEw9DnaQjB4KWMMLtcXi0WmlLP_heXh94oSVprNHKNSAN4pNXQnhMho4H9xfW_5GHw5fKGNyitZpBtDLjT_dGfPYrK-2OA5RNFHmt0U5JwDIySpAM5Tr0i12BZ-IZ66rWDHZ7Re24G1XJA6ZMDgsY8iDTru88SmAPtm9xw9J_djhQ==&uniplatform=NZKPT&language=CHS

summary:
  近年来,随着数字技术的快速发展,司法领域积累了大量法律文档和判例文本。多标签文本分类作为一种重要的自然语言处理技术,能够将单个文档归入多个相关类别,在司法文本处理方面展现出巨大潜力。随着深度学习的发展,基于深度学习的多标签文本分类方法成为当前研究的主流方向。现有的司法文本多标签分类任务主要基于案件事实描述,对罪名、法律条文和刑期进行预测。多标签文本分类技术能够显著提升法律文档的检索效率,使法官、律师等法律从业者更快速地找到相关案例和条款,同时减少人工干预与误差,确保相似案件的分类和处理一致性,从而节省大量时间和人力资源。然而,现有模型仍然存在以下问题:特征提取与利用不足、忽视标签与文本之间的关联、循环神经网络在提取全局信息时可能导致信息过度压缩或丢失、未充分建模标签之间的多重相关性,以及多头注意力机制在标签选择过程中未有效融合标签的局部与全局信息。针对上述问题,本文提出了两种基于深度学习的司法文本多标签分类模型: (1)针对不同层次特征提取与利用不足、标签与文本之间语义关联建模能力弱,以及循环神经网络在全局信息建模过程中可能导致信息丢失的问题,本文设计了一种基于多特征融合和标签嵌入的神经...

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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: 2025-09-16 13:05:29.287869
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYEw9DnaQjB4KWMMLtcXi0WmlLP_heXh94oSVprNHKNSABhsvA75RgBSuQIaxi73pWHOmYJzO7TyVNgDHLI-9N9ysiQ_ZR3lFrHtAp8xifbTmsFMSPYBxIaC0eLff89FErUPUHX38ulfFixtUqjTox-Hyg7dYaostelbI8jAO42WHA==&uniplatform=NZKPT&language=CHS

summary:
  海量文本分类是自然语言处理中的核心任务,在信息检索、舆情分析、自动推荐等多个领域具有广泛的应用价值。随着互联网和社交媒体的快速发展,面对高维、非结构和动态变化的文本数据,传统的基于规则和机器学习的方法已难以满足高效分类的需求。近年来,深度学习方法尤其是图神经网络和预训练语言模型,为文本分类提供了新的技术路径。其中,图卷积神经网络和BERT预训练语言模型分别在文本的全局拓扑信息建模和上下文语义表示方面具有优势,但各自仍存在局限性。图卷积神经网络依赖固定的静态邻接矩阵,难以适应不同文本样本的特征变化,影响模型的泛化能力;BERT预训练模型采用的掩码语言模型具有随机性,受限于固定的掩码策略,未能充分捕捉全局拓扑结构信息,限制了上下文语义建模能力,从而影响分类性能。针对上述问题,本文分别提出两种改进策略,以提升分类的准确性和泛化能力,主要研究内容如下: (1)针对传统图卷积神经网络在文本分类任务中受限于固定静态邻接矩阵,难以适应不同文本样本的特征差异,导致模型准确率受限的问题,本文提出了一种基于变分结构和动态边权重学习的图卷积神经网络改进模型(VSD-GCN),方法利用变分结构生成稳定的文本表示...

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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: 2025-09-16 13:05:29.288333
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYEw9DnaQjB4KWMMLtcXi0WmlLP_heXh94oSVprNHKNSAK1lB6WW8wpRMSAkdCBod1W44BBekau1ZICg91wlO0AUXz5MDYxHEsc_VIYuPsvIolEH-m8fz5EODcfVnJzxa_qaKXXfPX9rcDFwfwggPahyvEGDYkEEw1CMVMg6zdW9jw==&uniplatform=NZKPT&language=CHS

summary:
  医疗文本分类可以自动识别和归类海量医疗文本,方便医生快速检索相关信息,提高诊断效率和准确率。医疗文本分类是智慧医疗的核心技术之一,其发展将推动医疗信息化、智能化和个性化,最终惠及广大患者。然而医疗文本专业性强、术语密集,聚焦于医学知识和健康信息,对文本分类任务的准确性和严谨性要求极高。普通文本语言较通俗,更注重情感表达,分类任务相对灵活,数据处理和模型应用难度较低。针对现有方法常忽略文本结构信息和无法利用高阶邻居联系的缺陷,本文提出基于异构图扩散网络的医疗文本分类方法(Medical Graph Diffusion,MGD),用于医疗文本分类。MGD构建文本异构图以捕捉更多结构关系,并采用图扩散方法寻找长程依赖,再结合异构图卷积网络和多层感知器完成分类任务。主要内容如下: (1)设计并构建文本异构图。为解决现有方法常忽略文本信息、无法有效提取结构关系的问题,本文构建文本异构图以捕捉全局信息。该异构图涵盖单词-单词、句子-句子及单词-句子间的关系。特别地,添加句子-句子关系,弥补远程依赖捕捉难题,丰富信息维度。消融实验结果表明,文本异构图的构建显著提升了分类任务的性能,验证了其在本研究中的...

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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: 2025-09-16 13:05:29.288782
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYH8nYj6147OZQxhuIzaUjY0EoEyhPMLoagsAdxqfVO_PNU33aCYCo93S9ohlB_v_dT7CVjHOkARA28CHQyDXB9FK9ZAgZQUIUqBxkw91CONO3qlinxxFxjBMki_IwLDnLJSQfzpbk6Od_Ntmn-mV5J86CTvbrQUfuJnKvFEOR-MgA==&uniplatform=NZKPT&language=CHS

summary:
  随着医疗信息化进程的加速推进,癌症相关的医疗文本数据呈现指数级增长趋势,如何高效处理和分析这些专业文本数据成为医学信息学领域的重要课题。与普通文本相比,癌症相关文本在词汇和结构层面具有显著差异:一方面包含大量专业术语,另一方面呈现独特的文本组织形式。这种专业性和结构特殊性导致传统文本分类方法在癌症文本分类任务中往往表现欠佳。 本文系统性地梳理癌症文本分类技术在医学领域的应用现状,深入分析关键技术,并提出一种融合多视图和图卷积的癌症文本分类方法。研究表明,该方法在癌症文本分类任务当中优于其他现有方法,为后续癌症文本分类应用提供参考价值。主要研究内容包括: (1)通过文本表示学习方法,对文本进行表示学习,构建单词节点特征和句子节点特征。并根据两种节点表示构建两种视图,以确保每个单词节点的独特属性和句子节点的语义完整性得到保留。 (2)对于不同的视图分别使用特定的图卷积模块对其进行特征表示学习,通过该方法提取到单词节点视图和句子节点视图的整体特征表示,并对两种视图特征表示进行融合,得到文本的完整表示。通过该方法有效解决了癌症文本中存在较多专业术语,以及其独特结构带来的问题。 (3)通过在癌症文...

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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: 2025-09-16 13:05:29.289251
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=w3MPIJRjtYH8nYj6147OZQxhuIzaUjY0EoEyhPMLoagsAdxqfVO_PDQKKQ_AJgGKHrFmpgKkt3Nu2L2VsrcaxPtxz7Z_iYaPG9oSCVX_JOEsgbSLfh2bjA5lcurJADnkrmMfkE1qWpvA1Tt20Okf66Efj04r09rbtbrwW3gl-9br-awfnH-o25lUUKUbzEIp&uniplatform=NZKPT&language=CHS

summary:
  文本分类是自然语言处理领域的一项重要任务,主要目的是将文本数据归类到特定的类别中。图神经网络将文本之间的复杂关系通过某种方法关联起来,提升了文本分类的准确性,预训练模型依靠本身强大的语义表达力和迁移学习的优点,在各种文本分类相关的任务里都取得了明显成效。因此,有学者提出了融合两种方法的文本分类模型,通过预训练模型捕捉文本的深层语义信息,借助图神经网络构建文本之间的结构关系,提高了模型的分类效果。BertGCN通过联合训练BERT和图卷积网络(Graph Convolutional Networks,GCN)模块,在许多文本分类数据集上取得了优异的表现,虽然BERT模块能够有效地捕捉文本的语义信息,GCN模块能准确构建词与词之间的依赖关系,但是BertGCN仍然存在一些局限性,为了解决这些问题,本文做了以下工作: (1)BERT模块中的[MASK]标记会导致预训练与下游任务目标表征不一致的问题,因而模型缺乏对文本结构的表征能力,无法捕捉长距离依赖关系。针对这个问题,本文提出了一种基于隐层空间混合增强的文本分类方法(XLNet with Hidden State Mixing,XLNet M...

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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: 2025-09-16 13:05:29.289714
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAyGgg4aGM1OThqeQ%3D%3D

summary:
  [目的]解决常规文本分类任务中由于领域训练数据稀缺、类型间差异大等因素导致的分类准确率低的问题.[方法]引入深层金字塔卷积网络与多门控制单元机制,构建基于BERT-DPCNN-MMOE模型框架的分类模型,通过设计多任务实验和迁移学习实验,对标8种基线模型,验证本文分类模型的有效性.[结果]自主构建多任务跨类型的数据作为训练测试基础,发现本文模型在多任务实验和迁移学习实验中的分类效果均优于8种基线模型,F1值的提升幅度均超过4.7个百分点.[局限]模型在其他领域的适应性需进一步研究.[结论]基于BERT-DPCNN-MMOE分类模型在多任务、跨类型文本分类任务上能够表现出更优的效果,对未来专题情报分类任务具有重要意义.

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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: 2025-09-16 13:05:29.290153
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA0Ggg4aGM1OThqeQ%3D%3D

summary:
  [目的]减少文本分类过程中因语言差异和文本特征选择所导致的语义偏离和丢失,保留更多的文本信息.[方法]首先,采用SBERT的预训练模型进行句子表示;其次,使用句向量旋转相似度方法计算不同文本中句子的相似度并对文本内的句子加权形成文本向量;最后,集成机器学习和神经网络的分类方法完成跨语言文本分类.[结果]在中、英、俄、法、西班牙文等跨语言文本数据集和多语言公开数据集Reuters的分类任务上进行实验的结果表明,所提方法的准确率约为96％,较已有方法有显著提升,且在召回率、精确率和F1值等评价指标上均有所提升.[局限]未考虑句子在文本中的出现位置对其权重的影响.[结论]句向量加权的文本表征方法能够减少语义偏离和语义丢失,从而提升跨语言文本分类的性能.

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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: 2025-09-16 13:05:29.290620
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA1Ggg4aGM1OThqeQ%3D%3D

summary:
  [目的]对海量警情文本数据进行智能化分类.[方法]针对警情文本分类任务,提出一个基于BERT-DPCNN 的文本分类模型.采用BERT预训练模型生成文本词向量,通过优化DPCNN模型中的激活函数和改进动态学习率提高分类性能.[结果]将BERT-DPCNN与BERT、BERT-CNN、BERT-RCNN、BERT-RNN、BERT-LSTM、ERNIE等6类模型进行对比实验,结果表明BERT-DPCNN的准确率、召回率和精准率最佳.在二分类任务中,BERT-DPCNN的准确率达到98％以上,十一分类任务中,其准确率达到82％以上.[局限]模型参数较多,实验次数有限,有待进一步测试.[结论]基于BERT-DPCNN的文本分类模型能有效提升警情文本分类的准确率,为公安部门进行警情分析和研判提供数据支撑.

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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: 2025-09-16 13:05:29.291087
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA0MDEyGgg4aGM1OThqeQ%3D%3D

summary:
  [目的]利用解耦技术缓解过度平滑并构建深度图网络学习文本隐藏特征,同时采用注意力扩散机制增强图网络的长距离交互能力,以提升法律文本细粒度分类效果.[方法]提出基于深度注意力扩散图神经网络的法律文本细粒度分类模型FLGNN.首先使用预训练模型BERT作为嵌入层获取长距离语义特征,接着构建文本有向图通过深度图网络捕获文本全局图信息和隐藏特征,最后利用特征融合和节点级注意力机制优化文本特征并进行分类任务.[结果]模型在来自北大法宝数据库的PKULawData数据集上Acc达94.85％,较BERT、DADGNN和RCNN等基线模型分别提升了 1.15、3.44和1.72个百分点;在法律合同文本数据集JSCLawData上Acc达90.91％,较BERT、DADGNN和RCNN等基线模型分别提升了 1.35、4.19和4.10个百分点.[局限]模型在其他领域的适用性需要进一步探究.[结论]FLGNN模型能捕获法律文本的全局图信息并挖掘深层语义信息,进一步提升了法律文本细粒度分类效果,可为法律领域智能化管理和人工智能提供有效支撑.

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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: 2025-09-16 13:05:29.291545
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2JqeWRkeDIwMjUwMjAwMhoIOGhjNTk4ank%3D

summary:
  数据要素时代,对决策的影响力成为衡量开放科学数据集质量与价值的重要维度,自动、客观地从政府公文中提取和评价开放科学数据集的决策影响力成为可行的手段.政府公告文件通常是一种长文本,从其中判断和科学数据集相关度作为关键环节,可转化成长文本的分类任务.针对该问题,面向科学数据集决策影响力评价场景,提出了一种基于词性特征融合的长文本分类方法(LTC-WF).首先,将文本中的词语根据不同词性进行分类,将相同词性的短语进行整合.然后,将归类后的短语组和原文本分别进行嵌入表示.最后,为了验证融合词性信息的效果,分别设计了拼接融合单元和门控融合单元,其中门控融合单元将短语组嵌入向量和原文文本嵌入向量分别赋予不同权重进行聚合,生成文本的最终嵌入表示进行分类.通过在构建的科学数据政策数据集上的实验结果表明,该方法比现有主流方法取得了更好的性能,为实现评价科学数据集决策影响力提供了有效技术方案.

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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: 2025-09-16 13:05:29.292022
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTUaCDhoYzU5OGp5

summary:
  多标签文本分类旨在为文本分配若干预定义的标签或类别.为了充分发掘标签间的关联,目前的方法通常使用标签关系图并结合图神经网络获取标签特征表示.然而,这类方法过度依赖初始建图策略,忽视了当前文本中固有的标签相关性,使得分类结果更依赖于数据集统计信息,而容易忽视当前文本段中的标签相关信息.因此,提出一种基于特征融合动态图网络的多标签文本分类算法,设计动态图来建模当前文本中的标签相关性,并结合特征融合与图神经网络,形成基于当前文本的标签表示,并由此形成更为准确的多标签文本结果.随后,设计实验进行验证,在3个数据集实验结果表明,所提出的模型在多标签分类任务中取得优秀的性能,验证其有效性和可行性.

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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: 2025-09-16 13:05:29.292480
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdocXp5eWhqMjAyNTAzMDAxGgg4aGM1OThqeQ%3D%3D

summary:
  科学评估不同类型新能源产业政策实施效果的差异性,对于优化产业政策、推动产业高质量发展具有重要现实意义.基于2005-2022年新能源产业政策及相关数据,利用ERNIE-DPCNN对新能源产业政策进行多标签分类,并建立面板分位数回归模型,实证检验不同类型政策在不同分位数下对产业发展影响的差异性.研究表明:ERNIE-DPCNN模型可以有效提升新能源产业政策的分类效率,政策分类结果中环境型政策最多,尤其目标规划型政策,需求型政策的应用略显不足.供给型政策对新能源产业发展产生正向影响,并且在新能源发电量低、中、高地区对产业发展的影响力度逐渐降低;需求型政策在新能源发电量高的地区对产业发展产生正向影响.因此,在新能源产业发展模式成熟的地区,更加需要推广应用、政府采购等需求型政策推动产业高质量发展.

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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: 2025-09-16 13:05:29.292929
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGgg4aGM1OThqeQ%3D%3D

summary:
  [目的]基于意图识别构建更准确的语义相似度分类模型,为中文医疗健康查询服务提供更精准的答案匹配结果.[方法]融合BERT和卷积神经网络(CNN)构建意图识别模型,然后将其作为模型嵌入层构建嵌入意图识别的孪生BERT(ITBERT)语义分类模型.[结果]在CHIP-STS数据集上,相较于BERT和TextCNN单一模型,融合模型在意图识别的Top-1结果准确率分别提高了 1.5和8.2个百分点,达到了 73.6％;Top-3结果准确率分别提高了 3.2和7.6个百分点,达到了 91.2％,证明了融合模型对于意图识别效果的提升.对于语义相似度分类结果,相较于基准模型,ITBERT模型AUC值提高了 0.015～0.087,证明了意图知识的嵌入对医疗语义相似度分类效果的提升.[局限]人工标注的意图信息存在一定的偏差,可能会影响语义相似度的分类结果.[结论]融合模型可以改善医疗健康查询服务中的意图识别效果,嵌入识别的意图知识可以提高语义相似度分类模型的准确率,有利于提供更准确的医疗健康自动问答服务.

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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: 2025-09-16 13:05:29.293401
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoIOGhjNTk4ank%3D

summary:
  针对现有的情感分类方法普遍未能充分考虑用户个性化特征且忽略时间因素对情感分类结果的影响的问题,提出一种基于知识蒸馏和评论时间的文本情感分类新方法.首先,为解决数据集中高质量标注数据较少的问题,采用RoFormer-Sim生成模型对训练文本数据增强;然后,引入评论时间属性,从用户历史评论中提取用户的个性化信息,提出基于多特征融合的评论文本情感得分预测模型;最后,为提高针对冷启动用户的泛化性能,引入知识蒸馏理论,利用SKEP模型对基于多特征融合的情感分类模型进行通用性增强.在从中文股吧爬取的真实数据集上的实验结果表明:与SKEP、ELECTRA等典型方法相比,本文方法在准确率上分别提高了 3.1%和 0.9%,在F1 值上分别提高了2.7%和 1.0%,验证了其在改善情感分类表现方面的有效性.

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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: 2025-09-16 13:05:29.293863
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwMxoIOGhjNTk4ank%3D

summary:
  新闻主题文本内容简短却含义丰富,传统方法通常只考虑词粒度或句粒度向量中的一种进行研究,未能充分利用新闻主题文本不同粒度向量之间的关联信息.为深入挖掘文本的词向量和句向量间的依赖关系,提出一种基于XLNet和多粒度特征对比学习的新闻主题分类方法.首先,利用XLNet对新闻主题文本进行特征提取获得文本中词、句粒度的特征表示和潜在空间关系;然后,通过对比学习R-Drop策略生成不同粒度特征的正负样本对,以一定权重对文本的词向量-词向量、词向量-句向量和句向量-句向量进行特征相似度学习,使模型深入挖掘出字符属性和语句属性之间的关联信息,提升模型的表达能力.在THUCNews、Toutiao和SHNews数据集上进行实验,实验结果表明,与基准模型相比,所提方法在准确率和F1值上都有更好的表现,在三个数据集上的F1值分别达到了93.88％、90.08％、87.35％,验证了方法的有效性和合理性.

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author: Min Zeng;Jinfei Sun;Xueyou Luo;Caiquan Liu;Shiqi Zhang;Li Xie;Xiaoxin Chen
date: 15 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: 2025-09-16 13:05:29.297326
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: 2025-09-16 13:05:29.297794
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: 2025-09-16 13:05:29.298274
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: Gaurab Chhetri;Darrell Anderson;Boniphace Kutela;Subasish Das
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11443
pdf_url: https://arxiv.org/pdf/2509.11443
site: arxiv
title: A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm
created_at: 2025-09-16 13:05:29.298743
summary:
  Abstract:This study presents the first multi-platform sentiment analysis of public opinion on the 15-minute city concept across Twitter, Reddit, and news media. Using compressed transformer models and Llama-3-8B for annotation, we classify sentiment across heterogeneous text domains. Our pipeline handles long-form and short-form text, supports consistent annotation, and enables reproducible evaluation. We benchmark five models (DistilRoBERTa, DistilBERT, MiniLM, ELECTRA, TinyBERT) using stratified 5-fold cross-validation, reporting F1-score, AUC, and training time. DistilRoBERTa achieved the highest F1 (0.8292), TinyBERT the best efficiency, and MiniLM the best cross-platform consistency. Results show News data yields inflated performance due to class imbalance, Reddit suffers from summarization loss, and Twitter offers moderate challenge. Compressed models perform competitively, challenging assumptions that larger models are necessary. We identify platform-specific trade-offs and propose directions for scalable, real-world sentiment classification in urban planning discourse.

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author: Bowen Jing;Yang Cui;Tianpeng Huang
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11374
pdf_url: https://arxiv.org/pdf/2509.11374
site: arxiv
created_at: 2025-09-16 13:05:29.299208
summary:
  Abstract:In the era of large language model, relation extraction (RE) plays an important role in information extraction through the transformation of unstructured raw text into structured data (Wadhwa et al., 2023). In this paper, we systematically compare the performance of deep supervised learning approaches without transformers and those with transformers. We used a series of non-transformer architectures such as PA-LSTM(Zhang et al., 2017), C-GCN(Zhang et al., 2018), and AGGCN(attention guide GCN)(Guo et al., 2019), and a series of transformer architectures such as BERT, RoBERTa, and R-BERT(Wu and He, 2019). Our comparison included traditional metrics like micro F1, as well as evaluations in different scenarios, varying sentence lengths, and different percentages of the dataset for training. Our experiments were conducted on TACRED, TACREV, and RE-TACRED. The results show that transformer-based models outperform non-transformer models, achieving micro F1 scores of 80-90% compared to 64-67% for non-transformer models. Additionally, we briefly review the research journey in supervised relation classification and discuss the role and current status of large language models (LLMs) in relation extraction.

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title:
  Transformer Enhanced Relation Classification: A Comparative Analysis of Contextuality, Data Efficiency and Sequence Complexity


author: Cheng-Yang Tsai;Tzu-Wei Huang;Shao-Yu Wei;Guan-Wei Chen;Hung-Ying Chu;Yu-Cheng Lin
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11369
pdf_url: https://arxiv.org/pdf/2509.11369
site: arxiv
title: Decoding Musical Origins: Distinguishing Human and AI Composers
created_at: 2025-09-16 13:05:29.299684
summary:
  Abstract:With the rapid advancement of Large Language Models (LLMs), AI-driven music generation has become a vibrant and fruitful area of research. However, the representation of musical data remains a significant challenge. To address this, a novel, machine-learning-friendly music notation system, YNote, was developed. This study leverages YNote to train an effective classification model capable of distinguishing whether a piece of music was composed by a human (Native), a rule-based algorithm (Algorithm Generated), or an LLM (LLM Generated). We frame this as a text classification problem, applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to extract structural features from YNote sequences and using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The resulting model achieves an accuracy of 98.25%, successfully demonstrating that YNote retains sufficient stylistic information for analysis. More importantly, the model can identify the unique " technological fingerprints " left by different AI generation techniques, providing a powerful tool for tracing the origins of AI-generated content.

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author: Krystian Gajdzica;Bernhard Heim;Markus Neuhauser
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11246
pdf_url: https://arxiv.org/pdf/2509.11246
site: arxiv
title: Log-Concavity and Log-Convexity of Restricted Infinite Products
created_at: 2025-09-16 13:05:29.300145
summary:
  Abstract:In this paper we provide a classification on the sign distribution of \Delta _{E,\ell}(n):= p_{E,\ell }(n)^2 - p_{E,\ell }(n-1) \, p_{E,\ell }(n+1), where \begin{equation*} \sum_{n =0}^{\infty} p_{E,\ell }(n) \, q^n := \prod_{n \in S}
   \left(1 - q^n \right)^{-f_{\ell}(n)},\quad (\ell \in \mathbb{N}, f_1\equiv 1). \end{equation*} We take the product over 1\in S \subset \mathbb{N} and denote the complement by E, the set of exceptions. In the case of \ell=1 and E the multiples of k, p_{E,1}\left( n\right)  represents the number of k-regular partitions. More generally, let f_{\ell} satisfy a certain growth condition. We determine the signs of \Delta _{E,\ell }(n) for \ell large. The signs mainly depend on the occurrence of subsets of \{2,3,4,5\} as a part of the exception set and the residue class of n modulo  r, where r  depends on E. For example, let 2,3 \in S and 4 an exception. Let n be large. Then for almost all \ell we have \begin{equation*} \Delta _{E,\ell }(n) >0 \,\,\, \text{ for } n\equiv 2 \pmod{3}. \end{equation*} If we assume 3,4 \in S and 2 an exception. Let n be large. Then for almost all \ell we have \begin{equation*} \Delta _{E,\ell }(n) < 0 \,\,\, \text{ for } n\equiv 2 \pmod{3}. \end{equation*} Note that this property is independent of the integers k\in S,k>4.

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author: Erion Çano;Ivan Habernal
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11176
pdf_url: https://arxiv.org/pdf/2509.11176
site: arxiv
title: Differentially-private text generation degrades output language quality
created_at: 2025-09-16 13:05:29.300626
summary:
  Abstract:Ensuring user privacy by synthesizing data from large language models (LLMs) tuned under differential privacy (DP) has become popular recently. However, the impact of DP fine-tuned LLMs on the quality of the language and the utility of the texts they produce has not been investigated. In this work, we tune five LLMs with three corpora under four levels of privacy and assess the length, the grammatical correctness, and the lexical diversity of the text outputs they produce. We also probe the utility of the synthetic outputs in downstream classification tasks such as book genre recognition based on book descriptions and cause of death recognition based on verbal autopsies. The results indicate that LLMs tuned under stronger privacy constrains produce texts that are shorter by at least 77 %, that are less grammatically correct by at least 9 %, and are less diverse by at least 10 % in bi-gram diversity. Furthermore, the accuracy they reach in downstream classification tasks decreases, which might be detrimental to the usefulness of the generated synthetic data.

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author: Einari Vaaras;Manu Airaksinen
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11154
pdf_url: https://arxiv.org/pdf/2509.11154
site: arxiv
title: Feature Space Topology Control via Hopkins Loss
created_at: 2025-09-16 13:05:29.301084
summary:
  Abstract:Feature space topology refers to the organization of samples within the feature space. Modifying this topology can be beneficial in machine learning applications, including dimensionality reduction, generative modeling, transfer learning, and robustness to adversarial attacks. This paper introduces a novel loss function, Hopkins loss, which leverages the Hopkins statistic to enforce a desired feature space topology, which is in contrast to existing topology-related methods that aim to preserve input feature topology. We evaluate the effectiveness of Hopkins loss on speech, text, and image data in two scenarios: classification and dimensionality reduction using nonlinear bottleneck autoencoders. Our experiments show that integrating Hopkins loss into classification or dimensionality reduction has only a small impact on classification performance while providing the benefit of modifying feature topology.

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author: Hongxu Zhou;Hylke Westerdijk;Khondoker Ittehadul Islam
date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.11127
pdf_url: https://arxiv.org/pdf/2509.11127
site: arxiv
created_at: 2025-09-16 13:05:29.301537
summary:
  Abstract:This study investigates how context and emotional tone metadata influence large language model (LLM) reasoning and performance in fallacy classification tasks, particularly within political debate settings. Using data from U.S. presidential debates, we classify six fallacy types through various prompting strategies applied to the Qwen-3 (8B) model. We introduce two theoretically grounded Chain-of-Thought frameworks: Pragma-Dialectics and the Periodic Table of Arguments, and evaluate their effectiveness against a baseline prompt under three input settings: text-only, text with context, and text with both context and audio-based emotional tone metadata. Results suggest that while theoretical prompting can improve interpretability and, in some cases, accuracy, the addition of context and especially emotional tone metadata often leads to lowered performance. Emotional tone metadata biases the model toward labeling statements as \textit{Appeal to Emotion}, worsening logical reasoning. Overall, basic prompts often outperformed enhanced ones, suggesting that attention dilution from added inputs may worsen rather than improve fallacy classification in LLMs.

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title:
  Joint Effects of Argumentation Theory, Audio Modality and Data Enrichment on LLM-Based Fallacy Classification


author: 张典;刘畅;陈雯柏;缪祎晟;吴华瑞
keywords: 注意力机制;农业文本分类;深度金字塔卷积网络;预训练模型;MacBERT模型
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: 2025-09-16 13:05:29.304829
original_link:
  https://www.cqvip.com/doc/journal/3506615314?sign=e42a308a15bc5a438d633f7608747b481ad620272161f6147f482238173bb954&expireTime=1789535041535&resourceId=3506615314

summary:
  针对农业领域文本信息密度大、语义模糊、特征稀疏的特点,提出一种基于MacBERT(MLM as correction-BERT)、深度金字塔卷积网络(DPCNN)和注意力机制(Attention)的农业文本分类模型,命名为MacBERT—DPCNN—Attention(MDA)。首先,使用MacBERT模型充分提取农业类文本内容的上下文信息,强化文本的语义特征表示。然后,DPCNN模型通过其多层卷积神经网络和池化操作,有效捕获文本的局部特征。最后,注意力机制进一步增强农业文本序列的特征表达。结果表明,与其他主流模型如BERT—DPCNN、BERT—CNN、BERT—RNN相比,MDA模型在农业文本分类任务上的精确率提升1.04%以上,召回率提升0.95%以上,F1值提升0.14%以上。表明所提模型在解决农业领域文本分类问题方面的有效性和优越性。

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author: 李晨;刘纳;郑国风;杨杰;道路
keywords: 特征融合;双通道;短文本分类;DPECNN;BiLSTM;医疗文本挖掘
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: 2025-09-16 13:05:29.305300
original_link:
  https://www.cqvip.com/doc/journal/3502600080?sign=6e68e2819cd76876066e1eae76793f0d4a539e2a510388aeb97819a30f1c5e7b&expireTime=1789535041535&resourceId=3502600080

summary:
  针对医疗短文本存在特征稀疏、语义歧义和提取短文本特征不充分等问题,提出融合双通道特征的医疗短文本分类模型(EBDF)。该模型利用预训练模型得到动态词向量,使模型包含更丰富的语义信息;之后利用BiLSTM获取全局文本特征信息和DPECNN获取深层次的局部文本特征信息,为了提高模型的鲁棒性和泛化能力,采用FGM对抗训练算法对数据进行扰动;最后,将双通道的特征信息进行特征融合获得最终的文本表示。EBDF模型在三个医疗领域和两个通用领域的短文本数据集上与效果较好的模型相比,准确率提升约0.57%~6.16%,F1值提高约0.65%~5.80%。

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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: 2025-09-16 13:05:29.305799
original_link:
  https://www.cqvip.com/doc/journal/3505889201?sign=a5e271118f64012e6626f74327ce38fba572ad90ddebbc74b7a79187db93cfb8&expireTime=1789535041535&resourceId=3505889201

summary:
  针对现有的基于注意力机制的方法难以捕捉文本之间复杂的依赖关系的问题,提出一种基于对比学习增强双注意力机制的多标签文本分类方法。首先,分别学习基于自注意力和基于标签注意力的文本表示,并融合二者以获得更全面的文本表示捕捉文本的结构特征以及文本与标签之间的语义关联;其次,给定一个多标签对比学习目标,利用标签引导的文本相似度监督文本表示的学习,以捕捉文本之间在主题、内容和结构层面上复杂的依赖关系;最后,使用前馈神经网络作为分类器进行文本分类。实验结果表明,相较于LDGN(Label-specific Dual Graph neural Network),所提方法在EUR-Lex(European Union Law Document)数据集与Reuters-21578数据集上的排名第5处的归一化折现累积收益(nDCG@5)值分别提升了1.81和0.86个百分点,在AAPD(Arxiv Academic Paper Dataset)数据集与RCV1(Reuters Corpus VolumeⅠ)数据集上也都取得了有竞争力的结果。可见,所提方法能有效捕捉文本之间在主题、内容和结构层面上复杂的依赖关系,从而在多标签文本分类任务上取得较优结果。

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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: 2025-09-16 13:05:29.306279
original_link:
  https://www.cqvip.com/doc/journal/3501748678?sign=931615176261881f86348a0216d2ce591138000aaaea4fca5b78230de52e9689&expireTime=1789535041535&resourceId=3501748678

summary:
  近年来,图神经网络(GNN)在文本分类任务中受到广泛应用。当前基于GNN的文本分类模型首先将文本建模为图,然后使用GNN对文本图进行特征传播与聚合,但是此类方法有两点不足:一是现有模型由于图结构的限制无法捕获单词之间的高阶语义关系;二是现有模型无法捕获文本中的关键语义信息。为了解决上述问题,提出一种基于双超图卷积网络特征融合的文本分类模型。一方面,使用原始文本建立文本超图;另一方面,为短文本引入外部知识,使用基于SenticNet词库的外部知识对文本进行语义增强,构建语义超图。经过超图卷积后通过注意力机制对双超图特征进行融合,实现短文本分类。在4个文本分类数据集上的实验结果表明,该模型优于基线模型,具有优越的文本分类性能。

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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: 2025-09-16 13:05:29.306737
original_link:
  https://www.cqvip.com/doc/journal/3500943458?sign=0a594ff219fdfbe45614e8ba8becae13c1403d1b817ea44973125ee7c87cbef4&expireTime=1789535041535&resourceId=3500943458

summary:
  随着互联网技术的发展,产生了大量的各种背景下的文本信息,包括新闻资料,科学文献等等,这些文本信息所包含的内容并不统一.对于神经网络而言,不断地增加新领域的训练数据必然会导致对旧信息的灾难遗忘.因此在实际应用中,训练出可以终身学习的文本分类器具有很高的应用价值.为此,该文提出了一种基于网络剪枝和动态扩展策略的终身文本分类方法,适用于知识背景不断增加的文本分类场景.具体而言,该文将网络剪枝、参数正则化更新和网络扩展相结合,保证神经网络可以在不遗忘旧任务知识的前提下更有效的学习新任务.通过使用不确定正则化参数更新策略防止旧领域信息的遗忘,实现任务之间的知识迁移.在5个流行的文本分类数据集上进行终身学习的实验设置下进行验证,实验结果表明本文的方法与BERT预训练模型相比高出约10%,并且均优于其他终身学习的方法.

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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: 2025-09-16 13:05:29.307232
original_link:
  https://www.cqvip.com/doc/journal/3502552353?sign=9876c5e33f8811df4d2ab9a1dc59c1b058bdf8e5bc1bd444d74a310954485bbd&expireTime=1789535041535&resourceId=3502552353

summary:
  随着深度学习及相关预训练模型等在文本分类任务上取得较好表现,其模型泛化性要求与有限的数据规模之间的冲突愈发严峻。基于梯度下降的网络优化方法要求所做变换必须连续可微,且优化过程中容易囿于局部极小值。对此,提出一种基于格兰杰因果关系检验和最优传输理论的预训练模型性能优化方法。将随机化算法与数据驱动的概率分布算法结合,基于格兰杰因果关系检验在小样本数据集上生成有效特征。然后基于最优传输理论学习有效特征间结构化组合表征,进而兼容连续/非连续高维流形结构间的传输映射造成的不稳定性。实验表明,对比直接使用BERT和TextGCN主流预训练模型,在中英文数据集上准确率均有提升。

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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: 2025-09-16 13:05:29.307707
original_link:
  https://www.cqvip.com/doc/journal/3501769600?sign=06563b29b4186ca2f40fa674274c33ab1d9f9cb2de5b34b77c2851f922a9d290&expireTime=1789535041535&resourceId=3501769600

summary:
  施工方案是施工组织设计的核心环节。运用自然语言处理技术,从非结构化文本中提取施工方案,并予以审阅和审查,可有效提高施工方案的审查效率,提高编制质量,同时有利于发现施工方案中潜在的安全和质量风险,以便施工过程加以预警。从非结构化文本提取施工方案,需要明确不同类型的施工方案的内容构成,对相关段落进行内容归类。针对非结构化施工方案段落内容归类问题,在深入研究工程施工方案类别及内容构成框架的基础上,以城市管网工程施工组织设计段落为样本,进行了施工组织设计段落内容分类,提出了融合Albert、TextRCNN的段落文本分类模型,该模型采用Albert预训练语言模型进行词嵌入,将生成的词向量输入到TextRCNN分类器中完成文本分类,准确率提高0.79%,试验表明:结合Albert的TextRCNN可以有效对施工组织设计段落进行内容分类,为进一步施工方案提取提供基础。

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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: 2025-09-16 13:05:29.308183
original_link:
  https://www.cqvip.com/doc/journal/3501556210?sign=abdb0b8d5b84d0fcb67dff936edea2d7824dd2e2b631db62994d1451118a0c7a&expireTime=1789535041535&resourceId=3501556210

summary:
  针对图数据动态聚合未知邻节点学习能力难及融合语义特征不足造成的模型性能欠佳而分类准确率低的问题,提出了一种基于图神经网络(graph neural network,GNN)和注意力机制的分类模型——图注意力文本分类(graph attention text classification,GATC)。首先,构建了一种归纳式学习的图神经模型,利用聚合函数实现动态嵌入未知邻节点,增强模型泛化能力。其次,引入多头潜在注意力机制,通过低秩联合压缩技术减少推理键值缓存,显著地降低了内存占用,提高了模型性能。最后,融合GNN和门循环单元(gated recurrent unit,GRU)网络模型,进一步捕获图数据中结构和时序属性信息的语义特征,实现了特征的高效融合,并提升了模型的分类准确率。实验结果表明,所提方法既有效,又相比算法ADGL(adaptive dynamic graph learning)+MLA(multi-head latent attention)的分类准确率在CSI 100、CSI 300和Rus 1K数据集上分别提高至少4.0%、2.4%和3.1%。

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author: 尹照煜;宋文爱;刘宏昊
keywords: 数据增强;低资源文本分类;多目标知识蒸馏;自然语言处理;GPT-4;大型语言模型Claude3;Haiku
source: 现代电子技术
site: 维普
title: 硬件有限环境中低资源文本分类新策略
created_at: 2025-09-16 13:05:29.308663
original_link:
  https://www.cqvip.com/doc/journal/3483736790?sign=ee0f26927d5b8ad0978aee1361291f02fd1d9c5fafdbc8194c82b5018b2117ca&expireTime=1789535041535&resourceId=3483736790

summary:
  针对在硬件资源有限的情况下,难以有效提升低资源分类任务性能这一难题,提出使用基于新一代大型语言模型Claude3 Haiku的数据增强,并设计了一种更小更快的文本分类模型EQSBERT。首先基于Claude3 Haiku,使用分布式比例增强法和动态均衡过采样增强法来增强低资源文本分类任务数据集。通过二次自蒸馏、多目标知识蒸馏、多维剪枝,提出一种更小更快的BERT系列文本分类模型EQSBERT,采用EQSBERT对增强后的数据集进行分类。结果表明:Claude3 Haiku具有较好的性能,且成本效益高于GPT-4;EQSBERT在参数大幅减少的情况下也能维持其高性能,显著降低了运行成本。两者结合搭配使用,可以有效解决在硬件条件有限的情况下低资源分类任务方面的问题。该方案为资源受限情况下的自然语言处理任务提供了新的解决策略,在自动化数据标注、社交媒体监控以及内容审核系统应用方面有巨大潜力。

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author: 韦莉;赵德春;秦璐;刘洋华子;沈宇辰;叶昌荣
keywords: 先验知识;医疗实体标签语义;医疗问句分类;自适应融合机制
source: 生物医学工程学杂志
site: 维普
title: 融合医疗实体标签语义的医疗文本分类模型
created_at: 2025-09-16 13:05:29.309127
original_link:
  https://www.cqvip.com/doc/journal/3483995749?sign=853ff95885d252eac11412c535c915505314ec6119d5004c63fef1d218cc0803&expireTime=1789535041535&resourceId=3483995749

summary:
  医疗问句的自动分类对提升线上医疗服务的质量和效率具有重大意义,属于意图识别任务。联合实体识别和意图识别优于单任务模型的效果。目前,大多数公开医疗文本意图识别数据集缺乏实体标注,而人工标注这些实体耗时费力。为解决这一难题,本文提出一种融合医疗实体标签语义的医疗文本分类模型—基于转换器循环卷积神经网络实体标签语义的双向编码器表示(BRELS)。该模型首先利用自适应融合机制将医疗实体标签的先验知识融入,实现局部特征增强;然后在全局特征提取中,采用轻量化循环卷积神经网络,既抑制参数的增长,又保留文本的原始语义。本研究在三个公开医疗文本意图识别数据集上进行消融实验和对比实验以验证所提模型的性能;结果显示,该模型在各数据集上的F1值分别达到了87.34%、81.71%和77.74%。研究结果表明,BRELS模型能有效地识别和理解医疗术语,进而有效地识别用户的意图,提高了线上医疗服务的质量和效率。

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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: 2025-09-16 15:51:33.711430
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMORn1WcqoWJaI1-U9a-I2Kr7KYycp8ccQdRxk_-YXqq8VOFKTCweGa6sMGD1dw8FzBdhgYWuj9m1UVqb6cCzsxGP4U6S8k--1bLdRoIBhLCIYD9FZ-74H1xDef2FeBfQSe8yn2-WHbriRQw-RIyAY89Vn_ix3_AYVsapy-T5OSjA==&uniplatform=NZKPT&language=CHS

summary:
  以物理实验课后练习题文字回答的自动评分为例，探讨了基于中文文本分类技术在大学物理实验教学评价中的应用.依据SOLO分类理论制定了大学物理实验课后练习题文字回答的分类规则，在比较不同分类器性能指标后选择出最优分类器，实现对学生文字回答的自动分类和评价.结果表明，利用中文文本分类技术可实现学生物理实验课后练习题的文本自动分类，能够帮助教师对学生进行高效评价与分类统计，为教师教学提供有效的改进依据.

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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: 2025-09-16 15:51:33.712104
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMORn1WcqoWJaI1-U9a-I2KTUvkFclAguNZesPXBdMVnOrUYU1WkBNTOW0nsOBj5qC3smmFS_vQAgfgI1eVaidgStyLnqgPdxwJH1pK3eqXHKpuBJn4z6pfGD1zeGID6J4ez91PW2KW39DkFihX3seChDbODe0kJYVQ3EJrjAe-PA==&uniplatform=NZKPT&language=CHS

summary:
  随着互联网和通信技术的不断发展,社交媒体平台每天生成着大量的文本数据,这些数据不仅包含丰富的情感表达,还涉及多种文本类别。如何准确地进行情感分类以及对文本主题多分类,对社会生活具有重要的现实意义和价值。文本分类作为自然语言处理领域的重要内容,分类模型的性能一直深受该领域学者的研究。基于词典和规则的文本分类方法,可以通过预定义的词汇表或规则来直接判断文本的情感倾向或主题类别。基于机器学习的文本分类方法,能够通过学习标注文本中的语义特征,进而构建分类器对目标文本进行预测。基于深度学习的文本分类方法,利用多层非线性变换能够从文本数据中学习并提取到更深层次的语义特征,从而更准确地对文本类别或情感倾向进行分类。目前文本分类面临着更加复杂且细粒度化的情感表达,以及更为多样化的文本类别,为了应对这些挑战,本文提出了三种基于动态语义建模和特征提取的文本分类方法。具体工作内容如下: (1)针对句子级情感分类过程中句子独立于文档存在导致缺乏完整上下文语义的问题,提出一种通过权重分配策略增强局部情感特征的方法进行句子级情感分类。该方法先使用动态预训练模型对文本进行向量化表示,得到文本序列的动态词向量矩阵。再利...

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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: 2025-09-16 15:51:33.712612
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMORn1WcqoWJaI1-U9a-I2KTUvkFclAguNZesPXBdMVnMv646-IVeHTvprHzElBkkzDZ-YR9bY0LSQ323xIfhOkcjZ0_EFNfyPYi2JC9JAEbbvCjwD514srzvffoGvcliaTCSfmCf_ogxgi5M0ofBd8kl0wCCWgIh9a_df8L76fpCQ7dGNgcEs6&uniplatform=NZKPT&language=CHS

summary:
  文本分类作为自然语言处理领域的核心任务,其主要目标在于通过语义理解和信息归纳来优化用户的信息管理效率。在文本分类预测任务中,图卷积网络将输入文本构建为词-文档图结构,极大地提高了分类结果的精度,因此该模型被广泛应用于该领域。然而,其现有方法仅依赖词-文档图中的文档节点对文档类别进行预测,忽视了词节点在分类任务中的潜在辅助价值,存在语义利用不足的问题。此外,传统方法的词-文档图节点仅以独热编码作为初始化,存在特征表达能力不足以及文本语义提取不够深层的问题。针对以上问题,本文提出了两种基于图卷积网络的文本分类模型,研究了图卷积网络方法在文本分类中的应用,以进一步提高文本分类任务的准确率。论文的主要研究工作如下: (1)针对目前基于图卷积网络的文本分类方法对语义信息利用不够充分的问题,本文以经典的Text GCN为基础进行优化改进。首先,为了减小梯度消失问题并增强信息传递,本文采用Mish函数替换Text GCN中的Re LU激活函数。Mish函数具有平滑的梯度曲线,能够在深层网络中更好地传递梯度,从而提升模型的训练效果。其次,本文引入了词性标注和命名实体识别作为文本分类的辅助任务,结合多任务...

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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: 2025-09-16 15:51:33.713133
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPhl0U1GAHlBy-Jhreo3yADamIBflGHcpJpCXZJmORzhC2SOQER4xthYm-POqJ2-JPuWbToL8OqnDGHrf98wGljuNtpO_J02G6cL3cn4NdBszGdmBKQR97hc383EOOxp3apJ8Cd7Yz4sDrcKfYtga4Artfsl9oP4LSk7_i5cN1yoA==&uniplatform=NZKPT&language=CHS

summary:
  近年来,随着互联网新闻数据量的指数级增长和传播渠道的碎片化趋势,新闻文本分类技术在信息管理、个性化推荐及舆情监测等领域的重要性日益凸显。然而,现有方法在单标签新闻文本分类中面临上下文信息提取困难、语义特征模糊等问题,在层次标签分类中则存在标签层次信息利用不足、文本与结构交互效率低下的挑战。为克服这些局限,本文针对新闻文本分类的两种模式,分别提出了基于特征融合的单标签分类模型CBDN和基于动态层次标签融合的层次分类模型DHFPE,并通过系统实验验证了其性能与应用价值。 在单标签分类任务中,CBDN模型使用经过全词掩码策略改进的BERT-wwm做为嵌入层,通过全词掩码方法优化中文新闻文本的语义表示,生成高质量的词向量;引入长链接双向长短期记忆网络和多头注意力机制,分别捕捉时序依赖和多视角语义关联;采用改进的金字塔卷积神经网络提取多尺度局部特征,并通过加权融合与门控机制实现全局、时序及局部特征的动态交互。在层次分类任务中,DHFPE模型以Ro BERTa为基础,通过提示嵌入机制将层次标签路径融入文本输入,利用动态层次特征融合生成更具区分度的表示;引入基于注意力机制的对比学习策略,通过生成增强正...

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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: 2025-09-16 15:51:33.713613
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPhl0U1GAHlBy-Jhreo3yADamIBflGHcpJpCXZJmORzhEwAL3_94E0AmOJkK74-1TMCbuU7htUar_FfLeIVGE0Kw4ZTT0P9O7npTwFLzSDwUb7Xv5rSJW0qNgCxmuhQ6rqGuHozvKnNQkYGjKPh5XQvesQ4adRT7iSsnI6o1slJHw==&uniplatform=NZKPT&language=CHS

summary:
  近年来,随着数字技术的快速发展,司法领域积累了大量法律文档和判例文本。多标签文本分类作为一种重要的自然语言处理技术,能够将单个文档归入多个相关类别,在司法文本处理方面展现出巨大潜力。随着深度学习的发展,基于深度学习的多标签文本分类方法成为当前研究的主流方向。现有的司法文本多标签分类任务主要基于案件事实描述,对罪名、法律条文和刑期进行预测。多标签文本分类技术能够显著提升法律文档的检索效率,使法官、律师等法律从业者更快速地找到相关案例和条款,同时减少人工干预与误差,确保相似案件的分类和处理一致性,从而节省大量时间和人力资源。然而,现有模型仍然存在以下问题:特征提取与利用不足、忽视标签与文本之间的关联、循环神经网络在提取全局信息时可能导致信息过度压缩或丢失、未充分建模标签之间的多重相关性,以及多头注意力机制在标签选择过程中未有效融合标签的局部与全局信息。针对上述问题,本文提出了两种基于深度学习的司法文本多标签分类模型: (1)针对不同层次特征提取与利用不足、标签与文本之间语义关联建模能力弱,以及循环神经网络在全局信息建模过程中可能导致信息丢失的问题,本文设计了一种基于多特征融合和标签嵌入的神经...

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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: 2025-09-16 15:51:33.714076
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNTz6xOcPe5usEX2mlJTHCsjXrlIZ_oFFB9Kr7McgYy5jfIebVo180JL3J5PYGXKgKEDhyTELxVuKklMjDtS7q5TjHgSSzevh4QgJAybsQKn-fR5sx86jweQaTSIghe4ocOI93186EYZcFzV9Y_zdMvsH21Z3C7rAdDzgh8DBhudQ==&uniplatform=NZKPT&language=CHS

summary:
  海量文本分类是自然语言处理中的核心任务,在信息检索、舆情分析、自动推荐等多个领域具有广泛的应用价值。随着互联网和社交媒体的快速发展,面对高维、非结构和动态变化的文本数据,传统的基于规则和机器学习的方法已难以满足高效分类的需求。近年来,深度学习方法尤其是图神经网络和预训练语言模型,为文本分类提供了新的技术路径。其中,图卷积神经网络和BERT预训练语言模型分别在文本的全局拓扑信息建模和上下文语义表示方面具有优势,但各自仍存在局限性。图卷积神经网络依赖固定的静态邻接矩阵,难以适应不同文本样本的特征变化,影响模型的泛化能力;BERT预训练模型采用的掩码语言模型具有随机性,受限于固定的掩码策略,未能充分捕捉全局拓扑结构信息,限制了上下文语义建模能力,从而影响分类性能。针对上述问题,本文分别提出两种改进策略,以提升分类的准确性和泛化能力,主要研究内容如下: (1)针对传统图卷积神经网络在文本分类任务中受限于固定静态邻接矩阵,难以适应不同文本样本的特征差异,导致模型准确率受限的问题,本文提出了一种基于变分结构和动态边权重学习的图卷积神经网络改进模型(VSD-GCN),方法利用变分结构生成稳定的文本表示...

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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: 2025-09-16 15:51:33.714569
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNTz6xOcPe5usEX2mlJTHCsjXrlIZ_oFFB9Kr7McgYy5tO8ttNY9PnZYzDry2Uv2pmyDBOtPWkEp4H346YCddGrVda9eXVEqXfDCAVH0BEA9H1np13YXieUBn5bS7G8R4IhuIPJzXyIurWezjh2k9HVsyDJ-6ESbFbed_gXsyk1Vw==&uniplatform=NZKPT&language=CHS

summary:
  医疗文本分类可以自动识别和归类海量医疗文本,方便医生快速检索相关信息,提高诊断效率和准确率。医疗文本分类是智慧医疗的核心技术之一,其发展将推动医疗信息化、智能化和个性化,最终惠及广大患者。然而医疗文本专业性强、术语密集,聚焦于医学知识和健康信息,对文本分类任务的准确性和严谨性要求极高。普通文本语言较通俗,更注重情感表达,分类任务相对灵活,数据处理和模型应用难度较低。针对现有方法常忽略文本结构信息和无法利用高阶邻居联系的缺陷,本文提出基于异构图扩散网络的医疗文本分类方法(Medical Graph Diffusion,MGD),用于医疗文本分类。MGD构建文本异构图以捕捉更多结构关系,并采用图扩散方法寻找长程依赖,再结合异构图卷积网络和多层感知器完成分类任务。主要内容如下: (1)设计并构建文本异构图。为解决现有方法常忽略文本信息、无法有效提取结构关系的问题,本文构建文本异构图以捕捉全局信息。该异构图涵盖单词-单词、句子-句子及单词-句子间的关系。特别地,添加句子-句子关系,弥补远程依赖捕捉难题,丰富信息维度。消融实验结果表明,文本异构图的构建显著提升了分类任务的性能,验证了其在本研究中的...

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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: 2025-09-16 15:51:33.715038
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNTz6xOcPe5usEX2mlJTHCsjXrlIZ_oFFB9Kr7McgYy5hFU6nAmor8wtu1LywvfbjpxYPhxKC4E2L0EuuISHjUU2cLAaGdBJ9-ZiOb89PZtb7cq6Cx4LoJZqID71FNoKXOJWH9FbSzbuoINeCf1R3LR7aVSkWqxpSMZ19LJJbzizw==&uniplatform=NZKPT&language=CHS

summary:
  随着医疗信息化进程的加速推进,癌症相关的医疗文本数据呈现指数级增长趋势,如何高效处理和分析这些专业文本数据成为医学信息学领域的重要课题。与普通文本相比,癌症相关文本在词汇和结构层面具有显著差异:一方面包含大量专业术语,另一方面呈现独特的文本组织形式。这种专业性和结构特殊性导致传统文本分类方法在癌症文本分类任务中往往表现欠佳。 本文系统性地梳理癌症文本分类技术在医学领域的应用现状,深入分析关键技术,并提出一种融合多视图和图卷积的癌症文本分类方法。研究表明,该方法在癌症文本分类任务当中优于其他现有方法,为后续癌症文本分类应用提供参考价值。主要研究内容包括: (1)通过文本表示学习方法,对文本进行表示学习,构建单词节点特征和句子节点特征。并根据两种节点表示构建两种视图,以确保每个单词节点的独特属性和句子节点的语义完整性得到保留。 (2)对于不同的视图分别使用特定的图卷积模块对其进行特征表示学习,通过该方法提取到单词节点视图和句子节点视图的整体特征表示,并对两种视图特征表示进行融合,得到文本的完整表示。通过该方法有效解决了癌症文本中存在较多专业术语,以及其独特结构带来的问题。 (3)通过在癌症文...

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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: 2025-09-16 15:51:33.715550
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMoEQuni4kGG0VGHI5EYWSfa4-Olh4GSiKwyVNwmx8LzWy4RpLcdCPzQZWH1jmNY5E6J3KygOqM36fCnWUHqYSS4hFqOL0DptWcRrP3E4zIZsPWJoc9fZHfufbgabnCRdIZpHYVuKtQy1gAQY5xBUHeI4Fxyb7PNnm_vxzy98Rl6CjXNgANaRGU&uniplatform=NZKPT&language=CHS

summary:
  文本分类是自然语言处理领域的一项重要任务,主要目的是将文本数据归类到特定的类别中。图神经网络将文本之间的复杂关系通过某种方法关联起来,提升了文本分类的准确性,预训练模型依靠本身强大的语义表达力和迁移学习的优点,在各种文本分类相关的任务里都取得了明显成效。因此,有学者提出了融合两种方法的文本分类模型,通过预训练模型捕捉文本的深层语义信息,借助图神经网络构建文本之间的结构关系,提高了模型的分类效果。BertGCN通过联合训练BERT和图卷积网络(Graph Convolutional Networks,GCN)模块,在许多文本分类数据集上取得了优异的表现,虽然BERT模块能够有效地捕捉文本的语义信息,GCN模块能准确构建词与词之间的依赖关系,但是BertGCN仍然存在一些局限性,为了解决这些问题,本文做了以下工作: (1)BERT模块中的[MASK]标记会导致预训练与下游任务目标表征不一致的问题,因而模型缺乏对文本结构的表征能力,无法捕捉长距离依赖关系。针对这个问题,本文提出了一种基于隐层空间混合增强的文本分类方法(XLNet with Hidden State Mixing,XLNet M...

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author: 黎鉴斌
date: 2025-05-29
keywords: 图自监督对比学习;;长文本分类;;文本事件;;多关系;;负采样;
source: 中南林业科技大学
site: 知网
title: 基于图自监督对比学习的长文本分类方法研究
created_at: 2025-09-16 15:51:33.716030
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMoEQuni4kGG0VGHI5EYWSfa4-Olh4GSiKwyVNwmx8LzS5OP_jjUb1wsXY2CmjmdFMCHIX8CkwkCbAsEQzDXO87yamGaNDXmZMIfVP32GwK-8RKqnHjFapWsRmlcxaOfQnYpURSRp6sC4NAlnzHNzVVhVzQDw9-lpkeZn0jqZuDDQ==&uniplatform=NZKPT&language=CHS

summary:
  随着互联网的快速发展,长文本逐渐成为使用最广泛的信息载体。长文本分类作为有效的文本挖掘技术,普遍应用于新闻分类和垃圾邮件过滤。随着海量文本不断涌现,而现有的文本分类方法通常需要60%以上的标注数据才能达到最优效果,这使得数据标注成为繁琐的工作。因此,研究人员尝试将图自监督对比学习用于长文本分类任务,然而这仍然面临着巨大的挑战。第一个挑战,长文本是包含丰富语义信息的长序列,而现有方法难以识别文本中的核心特征。第二个挑战,目前的方法没有对负样本的选取进行区分,存在负采样偏差,会导致文本过度聚类问题。针对以上挑战,本文结合图自监督对比学习展开对长文本分类任务的方法研究,主要创新点如下: (1)提出了基于聚类负采样优化的图自监督对比方法ClusterText。该方法首先提取事件并通过计算其相似度来构建文本间的关系。由此构建的文本图包含了多种关系,有效整合了文本结构和语义信息。接着,提出了聚类优化策略以优化图对比阶段的负采样过程。具体来说,把文本聚类产生的伪标签作为自监督信号,从不同的聚类中选取节点作为锚节点的负样本集合,提高了负采样的准确率。此外,还设计了自我校正机制,利用节点间的距离长短来扩充...

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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: 2025-09-16 15:51:33.716490
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAyGghhd2p0NjZzcw%3D%3D

summary:
  [目的]解决常规文本分类任务中由于领域训练数据稀缺、类型间差异大等因素导致的分类准确率低的问题.[方法]引入深层金字塔卷积网络与多门控制单元机制,构建基于BERT-DPCNN-MMOE模型框架的分类模型,通过设计多任务实验和迁移学习实验,对标8种基线模型,验证本文分类模型的有效性.[结果]自主构建多任务跨类型的数据作为训练测试基础,发现本文模型在多任务实验和迁移学习实验中的分类效果均优于8种基线模型,F1值的提升幅度均超过4.7个百分点.[局限]模型在其他领域的适应性需进一步研究.[结论]基于BERT-DPCNN-MMOE分类模型在多任务、跨类型文本分类任务上能够表现出更优的效果,对未来专题情报分类任务具有重要意义.

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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: 2025-09-16 15:51:33.716947
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA0Gghhd2p0NjZzcw%3D%3D

summary:
  [目的]减少文本分类过程中因语言差异和文本特征选择所导致的语义偏离和丢失,保留更多的文本信息.[方法]首先,采用SBERT的预训练模型进行句子表示;其次,使用句向量旋转相似度方法计算不同文本中句子的相似度并对文本内的句子加权形成文本向量;最后,集成机器学习和神经网络的分类方法完成跨语言文本分类.[结果]在中、英、俄、法、西班牙文等跨语言文本数据集和多语言公开数据集Reuters的分类任务上进行实验的结果表明,所提方法的准确率约为96％,较已有方法有显著提升,且在召回率、精确率和F1值等评价指标上均有所提升.[局限]未考虑句子在文本中的出现位置对其权重的影响.[结论]句向量加权的文本表征方法能够减少语义偏离和语义丢失,从而提升跨语言文本分类的性能.

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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: 2025-09-16 15:51:33.717456
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA1Gghhd2p0NjZzcw%3D%3D

summary:
  [目的]对海量警情文本数据进行智能化分类.[方法]针对警情文本分类任务,提出一个基于BERT-DPCNN 的文本分类模型.采用BERT预训练模型生成文本词向量,通过优化DPCNN模型中的激活函数和改进动态学习率提高分类性能.[结果]将BERT-DPCNN与BERT、BERT-CNN、BERT-RCNN、BERT-RNN、BERT-LSTM、ERNIE等6类模型进行对比实验,结果表明BERT-DPCNN的准确率、召回率和精准率最佳.在二分类任务中,BERT-DPCNN的准确率达到98％以上,十一分类任务中,其准确率达到82％以上.[局限]模型参数较多,实验次数有限,有待进一步测试.[结论]基于BERT-DPCNN的文本分类模型能有效提升警情文本分类的准确率,为公安部门进行警情分析和研判提供数据支撑.

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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: 2025-09-16 15:51:33.717920
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA0MDEyGghhd2p0NjZzcw%3D%3D

summary:
  [目的]利用解耦技术缓解过度平滑并构建深度图网络学习文本隐藏特征,同时采用注意力扩散机制增强图网络的长距离交互能力,以提升法律文本细粒度分类效果.[方法]提出基于深度注意力扩散图神经网络的法律文本细粒度分类模型FLGNN.首先使用预训练模型BERT作为嵌入层获取长距离语义特征,接着构建文本有向图通过深度图网络捕获文本全局图信息和隐藏特征,最后利用特征融合和节点级注意力机制优化文本特征并进行分类任务.[结果]模型在来自北大法宝数据库的PKULawData数据集上Acc达94.85％,较BERT、DADGNN和RCNN等基线模型分别提升了 1.15、3.44和1.72个百分点;在法律合同文本数据集JSCLawData上Acc达90.91％,较BERT、DADGNN和RCNN等基线模型分别提升了 1.35、4.19和4.10个百分点.[局限]模型在其他领域的适用性需要进一步探究.[结论]FLGNN模型能捕获法律文本的全局图信息并挖掘深层语义信息,进一步提升了法律文本细粒度分类效果,可为法律领域智能化管理和人工智能提供有效支撑.

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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: 2025-09-16 15:51:33.718408
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2JqeWRkeDIwMjUwMjAwMhoIYXdqdDY2c3M%3D

summary:
  数据要素时代,对决策的影响力成为衡量开放科学数据集质量与价值的重要维度,自动、客观地从政府公文中提取和评价开放科学数据集的决策影响力成为可行的手段.政府公告文件通常是一种长文本,从其中判断和科学数据集相关度作为关键环节,可转化成长文本的分类任务.针对该问题,面向科学数据集决策影响力评价场景,提出了一种基于词性特征融合的长文本分类方法(LTC-WF).首先,将文本中的词语根据不同词性进行分类,将相同词性的短语进行整合.然后,将归类后的短语组和原文本分别进行嵌入表示.最后,为了验证融合词性信息的效果,分别设计了拼接融合单元和门控融合单元,其中门控融合单元将短语组嵌入向量和原文文本嵌入向量分别赋予不同权重进行聚合,生成文本的最终嵌入表示进行分类.通过在构建的科学数据政策数据集上的实验结果表明,该方法比现有主流方法取得了更好的性能,为实现评价科学数据集决策影响力提供了有效技术方案.

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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: 2025-09-16 15:51:33.718870
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTUaCGF3anQ2NnNz

summary:
  多标签文本分类旨在为文本分配若干预定义的标签或类别.为了充分发掘标签间的关联,目前的方法通常使用标签关系图并结合图神经网络获取标签特征表示.然而,这类方法过度依赖初始建图策略,忽视了当前文本中固有的标签相关性,使得分类结果更依赖于数据集统计信息,而容易忽视当前文本段中的标签相关信息.因此,提出一种基于特征融合动态图网络的多标签文本分类算法,设计动态图来建模当前文本中的标签相关性,并结合特征融合与图神经网络,形成基于当前文本的标签表示,并由此形成更为准确的多标签文本结果.随后,设计实验进行验证,在3个数据集实验结果表明,所提出的模型在多标签分类任务中取得优秀的性能,验证其有效性和可行性.

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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: 2025-09-16 15:51:33.719363
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdocXp5eWhqMjAyNTAzMDAxGghhd2p0NjZzcw%3D%3D

summary:
  科学评估不同类型新能源产业政策实施效果的差异性,对于优化产业政策、推动产业高质量发展具有重要现实意义.基于2005-2022年新能源产业政策及相关数据,利用ERNIE-DPCNN对新能源产业政策进行多标签分类,并建立面板分位数回归模型,实证检验不同类型政策在不同分位数下对产业发展影响的差异性.研究表明:ERNIE-DPCNN模型可以有效提升新能源产业政策的分类效率,政策分类结果中环境型政策最多,尤其目标规划型政策,需求型政策的应用略显不足.供给型政策对新能源产业发展产生正向影响,并且在新能源发电量低、中、高地区对产业发展的影响力度逐渐降低;需求型政策在新能源发电量高的地区对产业发展产生正向影响.因此,在新能源产业发展模式成熟的地区,更加需要推广应用、政府采购等需求型政策推动产业高质量发展.

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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: 2025-09-16 15:51:33.719864
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGghhd2p0NjZzcw%3D%3D

summary:
  [目的]基于意图识别构建更准确的语义相似度分类模型,为中文医疗健康查询服务提供更精准的答案匹配结果.[方法]融合BERT和卷积神经网络(CNN)构建意图识别模型,然后将其作为模型嵌入层构建嵌入意图识别的孪生BERT(ITBERT)语义分类模型.[结果]在CHIP-STS数据集上,相较于BERT和TextCNN单一模型,融合模型在意图识别的Top-1结果准确率分别提高了 1.5和8.2个百分点,达到了 73.6％;Top-3结果准确率分别提高了 3.2和7.6个百分点,达到了 91.2％,证明了融合模型对于意图识别效果的提升.对于语义相似度分类结果,相较于基准模型,ITBERT模型AUC值提高了 0.015～0.087,证明了意图知识的嵌入对医疗语义相似度分类效果的提升.[局限]人工标注的意图信息存在一定的偏差,可能会影响语义相似度的分类结果.[结论]融合模型可以改善医疗健康查询服务中的意图识别效果,嵌入识别的意图知识可以提高语义相似度分类模型的准确率,有利于提供更准确的医疗健康自动问答服务.

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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: 2025-09-16 15:51:33.720328
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoIYXdqdDY2c3M%3D

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: 2025-09-16 15:51:33.720819
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwMxoIYXdqdDY2c3M%3D

summary:
  新闻主题文本内容简短却含义丰富,传统方法通常只考虑词粒度或句粒度向量中的一种进行研究,未能充分利用新闻主题文本不同粒度向量之间的关联信息.为深入挖掘文本的词向量和句向量间的依赖关系,提出一种基于XLNet和多粒度特征对比学习的新闻主题分类方法.首先,利用XLNet对新闻主题文本进行特征提取获得文本中词、句粒度的特征表示和潜在空间关系;然后,通过对比学习R-Drop策略生成不同粒度特征的正负样本对,以一定权重对文本的词向量-词向量、词向量-句向量和句向量-句向量进行特征相似度学习,使模型深入挖掘出字符属性和语句属性之间的关联信息,提升模型的表达能力.在THUCNews、Toutiao和SHNews数据集上进行实验,实验结果表明,与基准模型相比,所提方法在准确率和F1值上都有更好的表现,在三个数据集上的F1值分别达到了93.88％、90.08％、87.35％,验证了方法的有效性和合理性.

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author: 张典;刘畅;陈雯柏;缪祎晟;吴华瑞
keywords: 注意力机制;农业文本分类;深度金字塔卷积网络;预训练模型;MacBERT模型
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: 2025-09-16 15:51:33.729544
original_link:
  https://www.cqvip.com/doc/journal/3506615314?sign=6bb9a1a1e1ed16b36ade236455af1b46deadcd119874f9766298c5fb40d589ce&expireTime=1789545002870&resourceId=3506615314

summary:
  针对农业领域文本信息密度大、语义模糊、特征稀疏的特点,提出一种基于MacBERT(MLM as correction-BERT)、深度金字塔卷积网络(DPCNN)和注意力机制(Attention)的农业文本分类模型,命名为MacBERT—DPCNN—Attention(MDA)。首先,使用MacBERT模型充分提取农业类文本内容的上下文信息,强化文本的语义特征表示。然后,DPCNN模型通过其多层卷积神经网络和池化操作,有效捕获文本的局部特征。最后,注意力机制进一步增强农业文本序列的特征表达。结果表明,与其他主流模型如BERT—DPCNN、BERT—CNN、BERT—RNN相比,MDA模型在农业文本分类任务上的精确率提升1.04%以上,召回率提升0.95%以上,F1值提升0.14%以上。表明所提模型在解决农业领域文本分类问题方面的有效性和优越性。

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author: 李晨;刘纳;郑国风;杨杰;道路
keywords: 特征融合;双通道;短文本分类;DPECNN;BiLSTM;医疗文本挖掘
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: 2025-09-16 15:51:33.730044
original_link:
  https://www.cqvip.com/doc/journal/3502600080?sign=d141c333d78a7bb5f32ce4f543d1c0329f6146adeb7f77578c9c7ac74c06e6f5&expireTime=1789545002870&resourceId=3502600080

summary:
  针对医疗短文本存在特征稀疏、语义歧义和提取短文本特征不充分等问题,提出融合双通道特征的医疗短文本分类模型(EBDF)。该模型利用预训练模型得到动态词向量,使模型包含更丰富的语义信息;之后利用BiLSTM获取全局文本特征信息和DPECNN获取深层次的局部文本特征信息,为了提高模型的鲁棒性和泛化能力,采用FGM对抗训练算法对数据进行扰动;最后,将双通道的特征信息进行特征融合获得最终的文本表示。EBDF模型在三个医疗领域和两个通用领域的短文本数据集上与效果较好的模型相比,准确率提升约0.57%~6.16%,F1值提高约0.65%~5.80%。

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author: 余明峰;秦永彬;黄瑞章;陈艳平;林川
keywords: 双注意力;多标签文本分类;自注意力;对比学习;标签注意力
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: 2025-09-16 15:51:33.730509
original_link:
  https://www.cqvip.com/doc/journal/3505889201?sign=bb53ed7f2787131752aa4f5e94feebbbbc88269981e9e72cd2630c931b160876&expireTime=1789545002870&resourceId=3505889201

summary:
  针对现有的基于注意力机制的方法难以捕捉文本之间复杂的依赖关系的问题,提出一种基于对比学习增强双注意力机制的多标签文本分类方法。首先,分别学习基于自注意力和基于标签注意力的文本表示,并融合二者以获得更全面的文本表示捕捉文本的结构特征以及文本与标签之间的语义关联;其次,给定一个多标签对比学习目标,利用标签引导的文本相似度监督文本表示的学习,以捕捉文本之间在主题、内容和结构层面上复杂的依赖关系;最后,使用前馈神经网络作为分类器进行文本分类。实验结果表明,相较于LDGN(Label-specific Dual Graph neural Network),所提方法在EUR-Lex(European Union Law Document)数据集与Reuters-21578数据集上的排名第5处的归一化折现累积收益(nDCG@5)值分别提升了1.81和0.86个百分点,在AAPD(Arxiv Academic Paper Dataset)数据集与RCV1(Reuters Corpus VolumeⅠ)数据集上也都取得了有竞争力的结果。可见,所提方法能有效捕捉文本之间在主题、内容和结构层面上复杂的依赖关系,从而在多标签文本分类任务上取得较优结果。

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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: 2025-09-16 15:51:33.730969
original_link:
  https://www.cqvip.com/doc/journal/3501748678?sign=54405b63757018b4abf4568484d6f9ab76551bca19077fa75742871a1775f942&expireTime=1789545002870&resourceId=3501748678

summary:
  近年来,图神经网络(GNN)在文本分类任务中受到广泛应用。当前基于GNN的文本分类模型首先将文本建模为图,然后使用GNN对文本图进行特征传播与聚合,但是此类方法有两点不足:一是现有模型由于图结构的限制无法捕获单词之间的高阶语义关系;二是现有模型无法捕获文本中的关键语义信息。为了解决上述问题,提出一种基于双超图卷积网络特征融合的文本分类模型。一方面,使用原始文本建立文本超图;另一方面,为短文本引入外部知识,使用基于SenticNet词库的外部知识对文本进行语义增强,构建语义超图。经过超图卷积后通过注意力机制对双超图特征进行融合,实现短文本分类。在4个文本分类数据集上的实验结果表明,该模型优于基线模型,具有优越的文本分类性能。

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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: 2025-09-16 15:51:33.731456
original_link:
  https://www.cqvip.com/doc/journal/3500943458?sign=d40b574a570e9dc84152a12abe057a40795ad57fa8d3b3eaa14dd0a516faf7bb&expireTime=1789545002870&resourceId=3500943458

summary:
  随着互联网技术的发展,产生了大量的各种背景下的文本信息,包括新闻资料,科学文献等等,这些文本信息所包含的内容并不统一.对于神经网络而言,不断地增加新领域的训练数据必然会导致对旧信息的灾难遗忘.因此在实际应用中,训练出可以终身学习的文本分类器具有很高的应用价值.为此,该文提出了一种基于网络剪枝和动态扩展策略的终身文本分类方法,适用于知识背景不断增加的文本分类场景.具体而言,该文将网络剪枝、参数正则化更新和网络扩展相结合,保证神经网络可以在不遗忘旧任务知识的前提下更有效的学习新任务.通过使用不确定正则化参数更新策略防止旧领域信息的遗忘,实现任务之间的知识迁移.在5个流行的文本分类数据集上进行终身学习的实验设置下进行验证,实验结果表明本文的方法与BERT预训练模型相比高出约10%,并且均优于其他终身学习的方法.

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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: 2025-09-16 15:51:33.731921
original_link:
  https://www.cqvip.com/doc/journal/3502552353?sign=eef08faca1f0afbbc8766a2e59464160eb7ce8c5444de726aa95cf3cd31cb552&expireTime=1789545002870&resourceId=3502552353

summary:
  随着深度学习及相关预训练模型等在文本分类任务上取得较好表现,其模型泛化性要求与有限的数据规模之间的冲突愈发严峻。基于梯度下降的网络优化方法要求所做变换必须连续可微,且优化过程中容易囿于局部极小值。对此,提出一种基于格兰杰因果关系检验和最优传输理论的预训练模型性能优化方法。将随机化算法与数据驱动的概率分布算法结合,基于格兰杰因果关系检验在小样本数据集上生成有效特征。然后基于最优传输理论学习有效特征间结构化组合表征,进而兼容连续/非连续高维流形结构间的传输映射造成的不稳定性。实验表明,对比直接使用BERT和TextGCN主流预训练模型,在中英文数据集上准确率均有提升。

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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: 2025-09-16 15:51:33.732367
original_link:
  https://www.cqvip.com/doc/journal/3501769600?sign=1be73104b77228b9a751482d36a9a4d567ad4cf344ed6fce6b3def8749c7bec3&expireTime=1789545002870&resourceId=3501769600

summary:
  施工方案是施工组织设计的核心环节。运用自然语言处理技术,从非结构化文本中提取施工方案,并予以审阅和审查,可有效提高施工方案的审查效率,提高编制质量,同时有利于发现施工方案中潜在的安全和质量风险,以便施工过程加以预警。从非结构化文本提取施工方案,需要明确不同类型的施工方案的内容构成,对相关段落进行内容归类。针对非结构化施工方案段落内容归类问题,在深入研究工程施工方案类别及内容构成框架的基础上,以城市管网工程施工组织设计段落为样本,进行了施工组织设计段落内容分类,提出了融合Albert、TextRCNN的段落文本分类模型,该模型采用Albert预训练语言模型进行词嵌入,将生成的词向量输入到TextRCNN分类器中完成文本分类,准确率提高0.79%,试验表明:结合Albert的TextRCNN可以有效对施工组织设计段落进行内容分类,为进一步施工方案提取提供基础。

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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: 2025-09-16 15:51:33.732856
original_link:
  https://www.cqvip.com/doc/journal/3501556210?sign=343894c797f62cba2468d37605aa0a50e6aa056225aa613d6181ee607b00894e&expireTime=1789545002870&resourceId=3501556210

summary:
  针对图数据动态聚合未知邻节点学习能力难及融合语义特征不足造成的模型性能欠佳而分类准确率低的问题,提出了一种基于图神经网络(graph neural network,GNN)和注意力机制的分类模型——图注意力文本分类(graph attention text classification,GATC)。首先,构建了一种归纳式学习的图神经模型,利用聚合函数实现动态嵌入未知邻节点,增强模型泛化能力。其次,引入多头潜在注意力机制,通过低秩联合压缩技术减少推理键值缓存,显著地降低了内存占用,提高了模型性能。最后,融合GNN和门循环单元(gated recurrent unit,GRU)网络模型,进一步捕获图数据中结构和时序属性信息的语义特征,实现了特征的高效融合,并提升了模型的分类准确率。实验结果表明,所提方法既有效,又相比算法ADGL(adaptive dynamic graph learning)+MLA(multi-head latent attention)的分类准确率在CSI 100、CSI 300和Rus 1K数据集上分别提高至少4.0%、2.4%和3.1%。

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author: 尹照煜;宋文爱;刘宏昊
keywords: 数据增强;低资源文本分类;多目标知识蒸馏;自然语言处理;GPT-4;大型语言模型Claude3;Haiku
source: 现代电子技术
site: 维普
title: 硬件有限环境中低资源文本分类新策略
created_at: 2025-09-16 15:51:33.733312
original_link:
  https://www.cqvip.com/doc/journal/3483736790?sign=489c7431c50e9c30af2485a16c7e2b15033c1bebfa352abe275c0767950bdaf2&expireTime=1789545002870&resourceId=3483736790

summary:
  针对在硬件资源有限的情况下,难以有效提升低资源分类任务性能这一难题,提出使用基于新一代大型语言模型Claude3 Haiku的数据增强,并设计了一种更小更快的文本分类模型EQSBERT。首先基于Claude3 Haiku,使用分布式比例增强法和动态均衡过采样增强法来增强低资源文本分类任务数据集。通过二次自蒸馏、多目标知识蒸馏、多维剪枝,提出一种更小更快的BERT系列文本分类模型EQSBERT,采用EQSBERT对增强后的数据集进行分类。结果表明:Claude3 Haiku具有较好的性能,且成本效益高于GPT-4;EQSBERT在参数大幅减少的情况下也能维持其高性能,显著降低了运行成本。两者结合搭配使用,可以有效解决在硬件条件有限的情况下低资源分类任务方面的问题。该方案为资源受限情况下的自然语言处理任务提供了新的解决策略,在自动化数据标注、社交媒体监控以及内容审核系统应用方面有巨大潜力。

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author: 韦莉;赵德春;秦璐;刘洋华子;沈宇辰;叶昌荣
keywords: 先验知识;医疗实体标签语义;医疗问句分类;自适应融合机制
source: 生物医学工程学杂志
site: 维普
title: 融合医疗实体标签语义的医疗文本分类模型
created_at: 2025-09-16 15:51:33.733780
original_link:
  https://www.cqvip.com/doc/journal/3483995749?sign=495ad301077ebc575aea01a0d9665dfc3d24cc0b7ff416e0a05edd8a18c49fa8&expireTime=1789545002870&resourceId=3483995749

summary:
  医疗问句的自动分类对提升线上医疗服务的质量和效率具有重大意义,属于意图识别任务。联合实体识别和意图识别优于单任务模型的效果。目前,大多数公开医疗文本意图识别数据集缺乏实体标注,而人工标注这些实体耗时费力。为解决这一难题,本文提出一种融合医疗实体标签语义的医疗文本分类模型—基于转换器循环卷积神经网络实体标签语义的双向编码器表示(BRELS)。该模型首先利用自适应融合机制将医疗实体标签的先验知识融入,实现局部特征增强;然后在全局特征提取中,采用轻量化循环卷积神经网络,既抑制参数的增长,又保留文本的原始语义。本研究在三个公开医疗文本意图识别数据集上进行消融实验和对比实验以验证所提模型的性能;结果显示,该模型在各数据集上的F1值分别达到了87.34%、81.71%和77.74%。研究结果表明,BRELS模型能有效地识别和理解医疗术语,进而有效地识别用户的意图,提高了线上医疗服务的质量和效率。

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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: 2025-09-16 15:59:47.179338
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMmuGUVqRDDS0t1NWNh0SfyaG9d8scePl4tk15GA4K5xwTWI8SoDAaG5H663CCGRivXgjvJo2a2igr4FHNI3z3Jg7MgtQVi_fSMagmWek-vTJAF4squdWn7ASxiSttPnjxUiAdZ1mgyrYdbq5nlDHPZHu5EWcK59Qu4ChWQEQWmAQ==&uniplatform=NZKPT&language=CHS

summary:
  传统文本分类模型存在分类精度有限、文本信息损失值高的不足。基于此，借鉴PVT模型的设计理念，通过缩放因子对查询向量实施同步缩放，实现对其核心注意力模块的改进，并结合BERT模型，提出BERT-sPTT文本分类模型。在THUNews数据集、复旦中文数据集、AG新闻数据集进行验证，实验结果显示三个数据集上该模型ACC值与F1值优于其他对比模型。该模型主要优势在于结合BERT捕捉文本深层语义信息方面的能力和PVT金字塔结构在特征提取和下采样方面的高效性。

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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: 2025-09-16 15:59:47.180029
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOtXcUU3MYnJEhNMCj7E25WwhM3IVA85_siXTTr34UrfxlmGAv7k1Cw4YFtIAmu9IHYjCuUaKEAsCK_0379JMOm1LZejaym8OgxAoETSY9QL-fbbF9e1coAgNn-kP3pgQ4FVJrikL4TgDRmkr13lk5GdrO1jKbdWTO07WNZAswy4Q==&uniplatform=NZKPT&language=CHS

summary:
[cell repeats an earlier one in this file: 198 characters]

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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: 2025-09-16 15:59:47.180535
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOtXcUU3MYnJEhNMCj7E25W-yv0M_AbmJFEzrpwZB6W7NVG3Ac8X1OR214jlkB81ZMZrMkOkdWX987KEY0xNdr1X0s5innLGYlUGmminMX6QCWKD_qP9z6yabCOwIktRqUM5MRBoV1S-NzuPjiwRj-j8cvOjSnb4rQEalwIw_GWdg==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u968f\u7740\u4e92\u8054\u7f51\u548c\u901a\u4fe1\u6280\u672f\u7684\u53d1\u5c55\uff0c\u793e\u4ea4\u5a92\u4f53\u5e73\u53f0\u4ea7\u751f\u4e86\u5927\u91cf\u5305\u542b\u60c5\u611f\u8868\u8fbe\u548c\u591a\u79cd\u4e3b\u9898\u7c7b\u522b\u7684\u6587\u672c\u6570\u636e\uff0c\u5982\u4f55\u51c6\u786e\u8fdb\u884c\u6587\u672c\u60c5\u611f\u548c\u591a\u5206\u7c7b\u95ee\u9898\u5177\u6709\u91cd\u8981\u7684\u73b0\u5b9e\u610f\u4e49\uff0c\u4e14\u6587\u672c\u5206\u7c7b\u6a21\u578b\u7684\u6027\u80fd\u5907\u53d7\u5173\u6ce8\u3002", "contribution": "\u8bba\u6587\u521b\u65b0\u6027\u5730\u5c06\u52a8\u6001\u8bed\u4e49\u5efa\u6a21\u3001\u7279\u5f81\u63d0\u53d6\u4ee5\u53ca\u6df1\u5ea6\u5b66\u4e60\u4e0e\u6ce8\u610f\u529b\u673a\u5236\u7ed3\u5408\uff0c\u63a8\u52a8\u4e86\u7ec6\u7c92\u5ea6\u60c5\u611f\u5206\u6790\u548c\u591a\u7c7b\u6587\u672c\u5206\u7c7b\u6280\u672f\u7684\u53d1\u5c55\u3002", "method": "\u7814\u7a76\u63d0\u51fa\u4e09\u79cd\u57fa\u4e8e\u52a8\u6001\u8bed\u4e49\u5efa\u6a21\u548c\u7279\u5f81\u63d0\u53d6\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u5305\u62ec\u5229\u7528\u52a8\u6001\u9884\u8bad\u7ec3\u6a21\u578b\u751f\u6210\u8bcd\u5411\u91cf\u3001\u7ed3\u5408\u6743\u91cd\u5206\u914d\u7b56\u7565\u3001\u5f15\u5165\u6ce8\u610f\u529b\u673a\u5236\u548c\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u6765\u589e\u5f3a\u60c5\u611f\u548c\u4e3b\u9898\u7279\u5f81\u7684\u8868\u8fbe\u4e0e\u6355\u6349\u3002", "objective": "\u9488\u5bf9\u590d\u6742\u3001\u7ec6\u7c92\u5ea6\u7684\u60c5\u611f\u8868\u8fbe\u548c\u591a\u6837\u5316\u6587\u672c\u7c7b\u522b\uff0c\u63d0\u51fa\u66f4\u6709\u6548\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u4ee5\u63d0\u5347\u60c5\u611f\u5206\u7c7b\u548c\u4e3b\u9898\u5206\u7c7b\u7684\u51c6\u786e\u7387\u3002", "results": "\u8fd9\u4e9b\u65b9\u6cd5\u63d0\u5347\u4e86\u6587\u672c\u60c5\u611f\u5206\u7c7b\u548c\u591a\u7c7b\u522b\u5206\u7c7b\u7684\u51c6\u786e\u6027\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u590d\u6742\u8bed\u4e49\u548c\u591a\u6837\u5316\u7c7b\u522b\u6587\u672c\u65f6\u8868\u73b0\u51fa\u66f4\u4f18\u7684\u6027\u80fd\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: 2025-09-16 15:59:47.181031
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNLWWlujdr_d2GdLpNnjxPpzJSIAQxoxqrpWyXwc_K8oTWy1XhzUJKGzSyw_4pTyzKZ-JLu8QktSdneRfBuzrFAxBAcEjhiG0Nf5zTIeXVFnRwkAT8R0TKuD00H8SfnFwyDWbwQtI-88JHohGwqczbhOEqQENxjgSbjQLtPyTXR5ToSP5-8hY0y&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\u9886\u57df\u7684\u91cd\u8981\u4efb\u52a1\uff0c\u901a\u8fc7\u8bed\u4e49\u7406\u89e3\u548c\u4fe1\u606f\u5f52\u7eb3\u5e2e\u52a9\u7528\u6237\u66f4\u9ad8\u6548\u5730\u7ba1\u7406\u4fe1\u606f\u3002\u4f20\u7edf\u7684\u57fa\u4e8e\u56fe\u5377\u79ef\u7f51\u7edc\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u867d\u7136\u63d0\u5347\u4e86\u5206\u7c7b\u7cbe\u5ea6\uff0c\u4f46\u5b58\u5728\u5bf9\u8bed\u4e49\u4fe1\u606f\u5229\u7528\u4e0d\u8db3\u548c\u7279\u5f81\u8868\u8fbe\u6709\u9650\u7684\u95ee\u9898\u3002", "contribution": "\u672c\u6587\u63d0\u51fa\u4e86\u878d\u5408\u591a\u4efb\u52a1\u5b66\u4e60\u548c\u65b0\u6fc0\u6d3b\u51fd\u6570\u7684\u56fe\u5377\u79ef\u7f51\u7edc\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u8fdb\u4e00\u6b65\u6316\u6398\u4e86\u8bcd\u8282\u70b9\u4fe1\u606f\uff0c\u63d0\u9ad8\u4e86\u6a21\u578b\u7684\u6027\u80fd\u548c\u5b9e\u7528\u6027\uff0c\u4e30\u5bcc\u4e86\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u7684\u7406\u8bba\u4e0e\u5b9e\u8df5\u3002", "method": "\u57fa\u4e8e\u7ecf\u5178\u7684Text GCN\u6a21\u578b\uff0c\u63d0\u51fa\u4e24\u79cd\u6539\u8fdb\u7684\u56fe\u5377\u79ef\u7f51\u7edc\u6587\u672c\u5206\u7c7b\u6a21\u578b\uff1a\u4e00\u662f\u7528Mish\u6fc0\u6d3b\u51fd\u6570\u66ff\u6362ReLU\u4ee5\u4f18\u5316\u68af\u5ea6\u4f20\u9012\uff0c\u4e8c\u662f\u5f15\u5165\u8bcd\u6027\u6807\u6ce8\u548c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u4f5c\u4e3a\u591a\u4efb\u52a1\u5b66\u4e60\u7684\u8f85\u52a9\u4efb\u52a1\u3002\u6b64\u5916\uff0c\u5173\u6ce8\u8bcd\u8282\u70b9\u5728\u5206\u7c7b\u4e2d\u7684\u8f85\u52a9\u4f5c\u7528\uff0c\u5e76\u6539\u8fdb\u8282\u70b9\u7684\u7279\u5f81\u8868\u8fbe\u65b9\u5f0f\u3002", "objective": "\u63d0\u5347\u56fe\u5377\u79ef\u7f51\u7edc\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u7684\u8bed\u4e49\u7406\u89e3\u548c\u7279\u5f81\u8868\u8fbe\u80fd\u529b\uff0c\u8fdb\u800c\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u7387\u3002", "results": "\u6539\u8fdb\u540e\u7684\u56fe\u5377\u79ef\u7f51\u7edc\u6a21\u578b\u6709\u6548\u589e\u5f3a\u4e86\u5bf9\u6587\u672c\u8bed\u4e49\u4fe1\u606f\u7684\u5229\u7528\u548c\u7279\u5f81\u8868\u8fbe\u80fd\u529b\uff0c\u5b9e\u73b0\u4e86\u6bd4\u4f20\u7edf\u65b9\u6cd5\u66f4\u9ad8\u7684\u6587\u672c\u5206\u7c7b\u51c6\u786e\u7387\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: 2025-09-16 15:59:47.181524
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNLWWlujdr_d2GdLpNnjxPpzJSIAQxoxqrpWyXwc_K8oaUAH_3EMnxZ-vbONSl_m0fvj1hfjm92Rboea8SNKGffSviWp4B5_8UxxS_t4TttW5L8j6ZxJYC4-O41hMhhDb3DEnH7VXzrz3PLT4ygjGUahsuxoZ8ziFCJ-aby4XMabw==&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\u91cf\u7684\u6025\u5267\u589e\u957f\u548c\u4f20\u64ad\u6e20\u9053\u7684\u788e\u7247\u5316\uff0c\u5982\u4f55\u9ad8\u6548\u3001\u51c6\u786e\u5730\u8fdb\u884c\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u5df2\u6210\u4e3a\u4fe1\u606f\u7ba1\u7406\u3001\u4e2a\u6027\u5316\u63a8\u8350\u548c\u8206\u60c5\u76d1\u6d4b\u7b49\u9886\u57df\u4e9f\u9700\u89e3\u51b3\u7684\u91cd\u8981\u95ee\u9898\u3002\u73b0\u6709\u65b9\u6cd5\u5728\u63d0\u53d6\u6587\u672c\u4e0a\u4e0b\u6587\u4fe1\u606f\u3001\u8bed\u4e49\u8868\u8fbe\u4ee5\u53ca\u5229\u7528\u5c42\u6b21\u6807\u7b7e\u7ed3\u6784\u65b9\u9762\u4ecd\u7136\u5b58\u5728\u6311\u6218\u3002", "contribution": "\u8bba\u6587\u5206\u522b\u9488\u5bf9\u5355\u6807\u7b7e\u548c\u5c42\u6b21\u6807\u7b7e\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u63d0\u51fa\u521b\u65b0\u6027\u7279\u5f81\u878d\u5408\u6a21\u578b\uff0c\u6709\u6548\u63d0\u5347\u4e86\u6587\u672c\u8bed\u4e49\u8868\u8fbe\u548c\u5c42\u6b21\u7ed3\u6784\u5229\u7528\u80fd\u529b\uff0c\u4e3a\u65b0\u95fb\u6587\u672c\u81ea\u52a8\u5206\u7c7b\u6280\u672f\u7684\u53d1\u5c55\u63d0\u4f9b\u4e86\u65b0\u7684\u601d\u8def\u548c\u65b9\u6cd5\u3002", "method": "\u9488\u5bf9\u5355\u6807\u7b7e\u5206\u7c7b\u4efb\u52a1\uff0c\u63d0\u51fa\u4e86\u878d\u5408\u5168\u8bcd\u63a9\u7801\u6539\u8fdb\u7684BERT-wwm\u5d4c\u5165\u3001\u591a\u5934\u6ce8\u610f\u529b\u3001\u53cc\u5411\u957f\u77ed\u671f\u8bb0\u5fc6\u548c\u91d1\u5b57\u5854\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684CBDN\u6a21\u578b\uff0c\u901a\u8fc7\u591a\u5143\u7279\u5f81\u52a0\u6743\u878d\u5408\u63d0\u5347\u6587\u672c\u8868\u8fbe\u3002\u5728\u5c42\u6b21\u5206\u7c7b\u4efb\u52a1\uff0c\u63d0\u51fa\u57fa\u4e8eRoBERTa\u548c\u63d0\u793a\u5d4c\u5165\u673a\u5236\u7684DHFPE\u6a21\u578b\uff0c\u52a8\u6001\u7ed3\u5408\u5c42\u6b21\u6807\u7b7e\u8def\u5f84\u548c\u5bf9\u6bd4\u5b66\u4e60\u7b56\u7565\uff0c\u4ee5\u83b7\u5f97\u66f4\u5177\u533a\u5206\u5ea6\u7684\u6587\u672c\u8868\u793a\u3002", "objective": "\u63d0\u51fa\u5e76\u4f18\u5316\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u4ee5\u63d0\u5347\u5355\u6807\u7b7e\u548c\u5c42\u6b21\u6807\u7b7e\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u7684\u6548\u679c\uff0c\u89e3\u51b3\u7279\u5f81\u63d0\u53d6\u548c\u6807\u7b7e\u5229\u7528\u7b49\u73b0\u6709\u74f6\u9888\u3002", "results": "\u7cfb\u7edf\u5b9e\u9a8c\u8868\u660e\uff0cCBDN\u548cDHFPE\u6a21\u578b\u5728\u5404\u81ea\u7684\u5206\u7c7b\u4efb\u52a1\u4e2d\u90fd\u663e\u8457\u63d0\u5347\u4e86\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u7387\u548c\u8868\u8fbe\u80fd\u529b\uff0c\u9a8c\u8bc1\u4e86\u7279\u5f81\u878d\u5408\u548c\u5c42\u6b21\u6807\u7b7e\u8def\u5f84\u52a8\u6001\u4ea4\u4e92\u7b56\u7565\u7684\u6709\u6548\u6027\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: 2025-09-16 15:59:47.182059
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNLWWlujdr_d2GdLpNnjxPpzJSIAQxoxqrpWyXwc_K8oQOpTtzsj4RMWRWJKE231yQD2Zoy47QQLlB1N9Pw9pMAyc9WAFRRVXtfch24si0iTE3Vd-9zi487V2fELKwDNUh7Aq2G-7K27Ow2fRkGMzM_c7QRIsXEwWeCDWt0MaGtdw==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u968f\u7740\u6570\u5b57\u6280\u672f\u7684\u53d1\u5c55\uff0c\u53f8\u6cd5\u9886\u57df\u79ef\u7d2f\u4e86\u6d77\u91cf\u6cd5\u5f8b\u6587\u6863\u548c\u5224\u4f8b\u6587\u672c\u3002\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u5728\u63d0\u5347\u6cd5\u5f8b\u6587\u6863\u68c0\u7d22\u6548\u7387\u3001\u51cf\u5c11\u4eba\u5de5\u5e72\u9884\u548c\u4fdd\u8bc1\u6848\u4ef6\u5904\u7406\u4e00\u81f4\u6027\u65b9\u9762\u5177\u6709\u91cd\u8981\u4f5c\u7528\uff0c\u4f46\u76ee\u524d\u9762\u4e34\u7279\u5f81\u63d0\u53d6\u4e0d\u8db3\u3001\u6807\u7b7e\u4e0e\u6587\u672c\u5173\u8054\u6027\u5f31\u4ee5\u53ca\u6807\u7b7e\u95f4\u591a\u91cd\u5173\u7cfb\u5efa\u6a21\u4e0d\u5145\u5206\u7b49\u6311\u6218\u3002", "contribution": "\u672c\u6587\u521b\u65b0\u6027\u5730\u7ed3\u5408\u591a\u7279\u5f81\u878d\u5408\u548c\u6807\u7b7e\u5d4c\u5165\u6280\u672f\uff0c\u9488\u5bf9\u53f8\u6cd5\u6587\u672c\u7684\u590d\u6742\u591a\u6807\u7b7e\u5206\u7c7b\u573a\u666f\uff0c\u63d0\u51fa\u9ad8\u6548\u6a21\u578b\uff0c\u4e3a\u6cd5\u5f8b\u6587\u6863\u667a\u80fd\u5904\u7406\u4e0e\u5e94\u7528\u63d0\u4f9b\u4e86\u65b0\u7684\u601d\u8def\u548c\u6709\u6548\u5de5\u5177\u3002", "method": "\u8bbe\u8ba1\u4e86\u4e24\u79cd\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u6a21\u578b\uff0c\u878d\u5408\u591a\u7279\u5f81\u548c\u6807\u7b7e\u8bed\u4e49\u5d4c\u5165\uff0c\u589e\u5f3a\u7279\u5f81\u8868\u8fbe\u80fd\u529b\uff0c\u5e76\u4f18\u5316\u5bf9\u6807\u7b7e\u4e0e\u6587\u672c\u3001\u6807\u7b7e\u95f4\u76f8\u5173\u6027\u7684\u5efa\u6a21\u3002", "objective": "\u9488\u5bf9\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u4e2d\u7684\u7279\u5f81\u63d0\u53d6\u3001\u6807\u7b7e\u4e0e\u6587\u672c\u5173\u8054\u548c\u6807\u7b7e\u95f4\u76f8\u5173\u6027\u5efa\u6a21\u7b49\u95ee\u9898\uff0c\u63d0\u51fa\u6539\u8fdb\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u63d0\u5347\u591a\u6807\u7b7e\u5206\u7c7b\u6027\u80fd\u3002", "results": "\u63d0\u51fa\u7684\u6a21\u578b\u6709\u6548\u7f13\u89e3\u4e86\u73b0\u6709\u65b9\u6cd5\u7279\u5f81\u63d0\u53d6\u548c\u6807\u7b7e\u5efa\u6a21\u7684\u4e0d\u8db3\uff0c\u63d0\u5347\u4e86\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\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: 2025-09-16 15:59:47.182557
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOGJ8Vq1iqkGCtyCSvvnympoFn3_WmieOirP6Y9-7UZmSIi8K6UhJibVVcJ3ABCw3iFbCS5NiVsAeE7awZyim4lXWxU_PhTzrvAa92sPIPzx4Xviz9Y5RPLl_QmSp9Q95iQJ5kKeC8UO9WEda3wsppSJZdHYf1tq6wyrTcFtizEqA==&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\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u6d77\u91cf\u6587\u672c\u6570\u636e\u5448\u73b0\u9ad8\u7ef4\u3001\u975e\u7ed3\u6784\u5316\u548c\u52a8\u6001\u53d8\u5316\u7279\u5f81\uff0c\u7ed9\u4fe1\u606f\u68c0\u7d22\u3001\u8206\u60c5\u5206\u6790\u548c\u81ea\u52a8\u63a8\u8350\u7b49\u9886\u57df\u7684\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u5e26\u6765\u4e86\u5de8\u5927\u6311\u6218\u3002\u4f20\u7edf\u57fa\u4e8e\u89c4\u5219\u548c\u673a\u5668\u5b66\u4e60\u7684\u65b9\u6cd5\u96be\u4ee5\u9ad8\u6548\u5904\u7406\u548c\u5206\u7c7b\u8fd9\u4e9b\u590d\u6742\u6570\u636e\uff0c\u56e0\u6b64\u8feb\u5207\u9700\u8981\u66f4\u5148\u8fdb\u7684\u6280\u672f\u624b\u6bb5\u3002", "contribution": "\u8bba\u6587\u7cfb\u7edf\u7814\u7a76\u5e76\u521b\u65b0\u6027\u5730\u63d0\u51fa\u4e86\u9002\u5408\u6d77\u91cf\u6587\u672c\u5206\u7c7b\u7684\u65b0\u578b\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u8fc7\u7ed3\u6784\u52a8\u6001\u8c03\u6574\u548c\u8bed\u4e49\u7279\u5f81\u878d\u5408\uff0c\u6709\u6548\u63d0\u5347\u4e86\u6a21\u578b\u6027\u80fd\uff0c\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u63d0\u4f9b\u4e86\u65b0\u7684\u6280\u672f\u601d\u8def\u3002", "method": "\u9488\u5bf9\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4f9d\u8d56\u56fa\u5b9a\u90bb\u63a5\u77e9\u9635\u7684\u95ee\u9898\uff0c\u63d0\u51fa\u57fa\u4e8e\u53d8\u5206\u7ed3\u6784\u548c\u52a8\u6001\u8fb9\u6743\u91cd\u7684\u6539\u8fdb\u6a21\u578b\uff08VSD-GCN\uff09\uff0c\u589e\u5f3a\u5bf9\u4e0d\u540c\u6587\u672c\u6837\u672c\u7279\u5f81\u7684\u9002\u5e94\u80fd\u529b\uff1b\u540c\u65f6\u9488\u5bf9BERT\u6a21\u578b\u8bed\u4e49\u5efa\u6a21\u5c40\u9650\u6027\uff0c\u63d0\u51fa\u591a\u5c42\u6b21\u6ce8\u610f\u529b\u673a\u5236\u548c\u7279\u5f81\u878d\u5408\u7b56\u7565\uff0c\u63d0\u5347\u5168\u5c40\u62d3\u6251\u548c\u4e0a\u4e0b\u6587\u4fe1\u606f\u7684\u8868\u8fbe\u80fd\u529b\u3002", "objective": "\u63d0\u51fa\u5e76\u7814\u7a76\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u6539\u8fdb\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u89e3\u51b3\u5f53\u524d\u56fe\u795e\u7ecf\u7f51\u7edc\u548c\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u5728\u5904\u7406\u591a\u6837\u5316\u6587\u672c\u65f6\u51c6\u786e\u6027\u548c\u6cdb\u5316\u80fd\u529b\u4e0d\u8db3\u7684\u95ee\u9898\u3002", "results": "\u6240\u63d0\u51fa\u7684\u6539\u8fdb\u6a21\u578b\u5728\u6587\u672c\u5206\u7c7b\u51c6\u786e\u6027\u548c\u6cdb\u5316\u80fd\u529b\u65b9\u9762\u5747\u4f18\u4e8e\u4f20\u7edf\u65b9\u6cd5\uff0c\u5b9e\u73b0\u4e86\u5bf9\u590d\u6742\u6587\u672c\u6570\u636e\u7684\u66f4\u9ad8\u6548\u5904\u7406\u548c\u5206\u7c7b\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: 2025-09-16 15:59:47.183037
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOGJ8Vq1iqkGCtyCSvvnympoFn3_WmieOirP6Y9-7UZmU2F_0hrjjH3SfEMLax4kkXypNb71zxdOGo_hK24yRkJr-ECdz9jkhQ2MQhD1ltb_EhKfzycEw88wuqyHlUu25_2RtBOrABuc3Cgcymh__aGUocs8K8ZA_GPvlsalc7gsQ==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u533b\u7597\u6587\u672c\u5206\u7c7b\u80fd\u591f\u81ea\u52a8\u8bc6\u522b\u548c\u6574\u7406\u6d77\u91cf\u7684\u533b\u7597\u6587\u672c\uff0c\u6709\u52a9\u4e8e\u533b\u751f\u5feb\u901f\u68c0\u7d22\u76f8\u5173\u4fe1\u606f\uff0c\u63d0\u9ad8\u8bca\u65ad\u6548\u7387\u548c\u51c6\u786e\u7387\u3002\u7531\u4e8e\u533b\u7597\u6587\u672c\u4e13\u4e1a\u6027\u5f3a\u4e14\u6d89\u53ca\u5927\u91cf\u672f\u8bed\uff0c\u5bf9\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u4e25\u8c28\u6027\u63d0\u51fa\u4e86\u6781\u9ad8\u8981\u6c42\uff0c\u7136\u800c\u73b0\u6709\u65b9\u6cd5\u5f80\u5f80\u5ffd\u89c6\u6587\u672c\u7ed3\u6784\u4fe1\u606f\u4e0e\u9ad8\u9636\u90bb\u5c45\u5173\u7cfb\u7684\u5229\u7528\uff0c\u96be\u4ee5\u6ee1\u8db3\u5b9e\u9645\u9700\u6c42\u3002", "contribution": "\u672c\u7814\u7a76\u521b\u65b0\u6027\u5730\u5c06\u5f02\u6784\u56fe\u6269\u6563\u7f51\u7edc\u5e94\u7528\u4e8e\u533b\u7597\u6587\u672c\u5206\u7c7b\uff0c\u901a\u8fc7\u7ed3\u6784\u5316\u5730\u6355\u6349\u6587\u672c\u5185\u90e8\u590d\u6742\u5173\u7cfb\uff0c\u6709\u6548\u63d0\u5347\u4e86\u5206\u7c7b\u6548\u679c\uff0c\u4e3a\u667a\u6167\u533b\u7597\u4e2d\u7684\u6587\u672c\u5904\u7406\u4efb\u52a1\u63d0\u4f9b\u4e86\u65b0\u7684\u65b9\u6cd5\u548c\u601d\u8def\u3002", "method": "\u672c\u6587\u63d0\u51fa\u57fa\u4e8e\u5f02\u6784\u56fe\u6269\u6563\u7f51\u7edc\uff08MGD\uff09\u7684\u533b\u7597\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u901a\u8fc7\u6784\u5efa\u5305\u542b\u5355\u8bcd-\u5355\u8bcd\u3001\u53e5\u5b50-\u53e5\u5b50\u548c\u5355\u8bcd-\u53e5\u5b50\u5173\u7cfb\u7684\u6587\u672c\u5f02\u6784\u56fe\uff0c\u7ed3\u5408\u56fe\u6269\u6563\u673a\u5236\u6355\u83b7\u957f\u7a0b\u4f9d\u8d56\uff0c\u5e76\u5229\u7528\u5f02\u6784\u56fe\u5377\u79ef\u7f51\u7edc\u548c\u591a\u5c42\u611f\u77e5\u673a\u8fdb\u884c\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u80fd\u591f\u66f4\u597d\u6355\u6349\u533b\u7597\u6587\u672c\u7ed3\u6784\u4fe1\u606f\u548c\u9ad8\u9636\u4f9d\u8d56\u5173\u7cfb\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u4ee5\u63d0\u5347\u533b\u7597\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u5b9e\u7528\u6027\u3002", "results": "\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u8be5\u65b9\u6cd5\u901a\u8fc7\u5f15\u5165\u548c\u4f18\u5316\u6587\u672c\u5f02\u6784\u56fe\u548c\u56fe\u6269\u6563\u673a\u5236\uff0c\u663e\u8457\u63d0\u5347\u4e86\u533b\u7597\u6587\u672c\u5206\u7c7b\u7684\u6027\u80fd\uff0c\u7279\u522b\u662f\u5728\u590d\u6742\u7ed3\u6784\u4fe1\u606f\u7684\u63d0\u53d6\u548c\u8fdc\u7a0b\u4f9d\u8d56\u6355\u6349\u65b9\u9762\u8868\u73b0\u7a81\u51fa\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: 2025-09-16 15:59:47.183509
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOGJ8Vq1iqkGCtyCSvvnympoFn3_WmieOirP6Y9-7UZmUOeY_ALduxPn1T7Btg6Ms5fDef2FmA4AH7tTuMffJJ0neOfryy6nO31l4Y3dus9UT5JytQ9HZVM_RYIGmPAU-7OK7P2f46wVuFb8O0tNLcows8x3v7wHWLO8f2xAF79Uw==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u968f\u7740\u533b\u7597\u4fe1\u606f\u5316\u7684\u63a8\u8fdb\uff0c\u764c\u75c7\u76f8\u5173\u533b\u7597\u6587\u672c\u6570\u636e\u8fc5\u901f\u589e\u957f\uff0c\u8fd9\u4e9b\u6587\u672c\u56e0\u542b\u6709\u4e13\u4e1a\u672f\u8bed\u548c\u7279\u6b8a\u7ec4\u7ec7\u7ed3\u6784\uff0c\u5bfc\u81f4\u4f20\u7edf\u5206\u7c7b\u65b9\u6cd5\u6548\u679c\u6b20\u4f73\uff0c\u9ad8\u6548\u5206\u6790\u764c\u75c7\u6587\u672c\u6210\u4e3a\u533b\u5b66\u4fe1\u606f\u5b66\u7684\u91cd\u8981\u7814\u7a76\u8bfe\u9898\u3002", "contribution": "\u521b\u65b0\u6027\u5730\u878d\u5408\u591a\u89c6\u56fe\u4e0e\u56fe\u5377\u79ef\u6280\u672f\uff0c\u63d0\u5347\u4e86\u764c\u75c7\u6587\u672c\u7684\u5206\u7c7b\u6548\u679c\uff0c\u4e3a\u533b\u5b66\u6587\u672c\u5206\u6790\u65b9\u6cd5\u63d0\u4f9b\u4e86\u65b0\u7684\u601d\u8def\u548c\u6280\u672f\u53c2\u8003\u3002", "method": "\u901a\u8fc7\u6587\u672c\u8868\u793a\u5b66\u4e60\uff0c\u5bf9\u5355\u8bcd\u548c\u53e5\u5b50\u8282\u70b9\u6784\u5efa\u591a\u89c6\u56fe\u7ed3\u6784\uff0c\u5206\u522b\u91c7\u7528\u56fe\u5377\u79ef\u6a21\u5757\u8fdb\u884c\u7279\u5f81\u5b66\u4e60\uff0c\u5e76\u878d\u5408\u4e24\u79cd\u89c6\u56fe\u7684\u7279\u5f81\uff0c\u83b7\u53d6\u764c\u75c7\u6587\u672c\u7684\u5b8c\u6574\u8868\u793a\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1\u3002", "objective": "\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\u4ee5\u63d0\u5347\u764c\u75c7\u6587\u672c\u7684\u5206\u7c7b\u51c6\u786e\u6027\uff0c\u4f18\u5316\u5e94\u5bf9\u4e13\u4e1a\u6027\u548c\u7ed3\u6784\u7279\u6b8a\u6027\u5e26\u6765\u7684\u6311\u6218\u3002", "results": "\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\u5728\u764c\u75c7\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u8868\u73b0\u4f18\u4e8e\u73b0\u6709\u65b9\u6cd5\uff0c\u80fd\u591f\u6709\u6548\u5904\u7406\u5927\u91cf\u4e13\u4e1a\u672f\u8bed\u53ca\u72ec\u7279\u6587\u672c\u7ed3\u6784\uff0c\u63d0\u5347\u5206\u7c7b\u6027\u80fd\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: 2025-09-16 15:59:47.184001
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPonYYJ4BAI_x9bXIHmh7pA96J8b8SggfRK_x3DkhbEnI75beLxSEtNCXr6sCoeXcQYSB4UayY6H-th5iGjAsq5Ex0C3d2mCCceRKYW1dzMCDDV481ve8I_zCQFUz4xn8AE6DCNBb1t1yVcnkA37knKo_fLFYFp9QpnYwOWHdiQkU4_cMWSr2xT&uniplatform=NZKPT&language=CHS

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

parsed_summary:
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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: 2025-09-16 15:59:47.184461
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAyGghlbXl0ejNrMg%3D%3D

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

parsed_summary:
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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: 2025-09-16 15:59:47.184920
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA0GghlbXl0ejNrMg%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: 2025-09-16 15:59:47.185396
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA1GghlbXl0ejNrMg%3D%3D

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

parsed_summary:
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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: 2025-09-16 15:59:47.185852
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA0MDEyGghlbXl0ejNrMg%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: 2025-09-16 15:59:47.186314
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2JqeWRkeDIwMjUwMjAwMhoIZW15dHozazI%3D

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

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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: 2025-09-16 15:59:47.186786
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTUaCGVteXR6M2sy

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: 2025-09-16 15:59:47.187253
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdocXp5eWhqMjAyNTAzMDAxGghlbXl0ejNrMg%3D%3D

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: 2025-09-16 15:59:47.187726
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGghlbXl0ejNrMg%3D%3D

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: 2025-09-16 15:59:47.188209
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoIZW15dHozazI%3D

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: 2025-09-16 15:59:47.188680
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwMxoIZW15dHozazI%3D

summary:
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author: 张典;刘畅;陈雯柏;缪祎晟;吴华瑞
keywords: 注意力机制;农业文本分类;深度金字塔卷积网络;预训练模型;MacBERT模型
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: 2025-09-16 15:59:47.195097
original_link:
  https://www.cqvip.com/doc/journal/3506615314?sign=8eb5688aeb6372de163c19dcacbee16861ef8f3ef7a3413dc19b09a680b6e292&expireTime=1789545494755&resourceId=3506615314

summary:
  针对农业领域文本信息密度大、语义模糊、特征稀疏的特点,提出一种基于MacBERT(MLM as correction-BERT)、深度金字塔卷积网络(DPCNN)和注意力机制(Attention)的农业文本分类模型,命名为MacBERT—DPCNN—Attention(MDA)。首先,使用MacBERT模型充分提取农业类文本内容的上下文信息,强化文本的语义特征表示。然后,DPCNN模型通过其多层卷积神经网络和池化操作,有效捕获文本的局部特征。最后,注意力机制进一步增强农业文本序列的特征表达。结果表明,与其他主流模型如BERT—DPCNN、BERT—CNN、BERT—RNN相比,MDA模型在农业文本分类任务上的精确率提升1.04%以上,召回率提升0.95%以上,F1值提升0.14%以上。表明所提模型在解决农业领域文本分类问题方面的有效性和优越性。

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author: 李晨;刘纳;郑国风;杨杰;道路
keywords: 特征融合;双通道;短文本分类;DPECNN;BiLSTM;医疗文本挖掘
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: 2025-09-16 15:59:47.195623
original_link:
  https://www.cqvip.com/doc/journal/3502600080?sign=178606afa59f8714e22b822e1c67464d2856bf8022f60c7f064eee2c0e2e609c&expireTime=1789545494755&resourceId=3502600080

summary:
  针对医疗短文本存在特征稀疏、语义歧义和提取短文本特征不充分等问题,提出融合双通道特征的医疗短文本分类模型(EBDF)。该模型利用预训练模型得到动态词向量,使模型包含更丰富的语义信息;之后利用BiLSTM获取全局文本特征信息和DPECNN获取深层次的局部文本特征信息,为了提高模型的鲁棒性和泛化能力,采用FGM对抗训练算法对数据进行扰动;最后,将双通道的特征信息进行特征融合获得最终的文本表示。EBDF模型在三个医疗领域和两个通用领域的短文本数据集上与效果较好的模型相比,准确率提升约0.57%~6.16%,F1值提高约0.65%~5.80%。

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author: 余明峰;秦永彬;黄瑞章;陈艳平;林川
keywords: 双注意力;多标签文本分类;自注意力;对比学习;标签注意力
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: 2025-09-16 15:59:47.196106
original_link:
  https://www.cqvip.com/doc/journal/3505889201?sign=7b7077493f1e42c0b331ffc8eac583b1cfef5166cacaadba3e81809ddb029b6d&expireTime=1789545494755&resourceId=3505889201

summary:
  针对现有的基于注意力机制的方法难以捕捉文本之间复杂的依赖关系的问题,提出一种基于对比学习增强双注意力机制的多标签文本分类方法。首先,分别学习基于自注意力和基于标签注意力的文本表示,并融合二者以获得更全面的文本表示捕捉文本的结构特征以及文本与标签之间的语义关联;其次,给定一个多标签对比学习目标,利用标签引导的文本相似度监督文本表示的学习,以捕捉文本之间在主题、内容和结构层面上复杂的依赖关系;最后,使用前馈神经网络作为分类器进行文本分类。实验结果表明,相较于LDGN(Label-specific Dual Graph neural Network),所提方法在EUR-Lex(European Union Law Document)数据集与Reuters-21578数据集上的排名第5处的归一化折现累积收益(nDCG@5)值分别提升了1.81和0.86个百分点,在AAPD(Arxiv Academic Paper Dataset)数据集与RCV1(Reuters Corpus VolumeⅠ)数据集上也都取得了有竞争力的结果。可见,所提方法能有效捕捉文本之间在主题、内容和结构层面上复杂的依赖关系,从而在多标签文本分类任务上取得较优结果。

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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: 2025-09-16 15:59:47.196588
original_link:
  https://www.cqvip.com/doc/journal/3501748678?sign=95c1767c1f582426c24791ae8444e366d6c746c7bda6032ce33f1cf4851fcc0c&expireTime=1789545494755&resourceId=3501748678

summary:
  近年来,图神经网络(GNN)在文本分类任务中受到广泛应用。当前基于GNN的文本分类模型首先将文本建模为图,然后使用GNN对文本图进行特征传播与聚合,但是此类方法有两点不足:一是现有模型由于图结构的限制无法捕获单词之间的高阶语义关系;二是现有模型无法捕获文本中的关键语义信息。为了解决上述问题,提出一种基于双超图卷积网络特征融合的文本分类模型。一方面,使用原始文本建立文本超图;另一方面,为短文本引入外部知识,使用基于SenticNet词库的外部知识对文本进行语义增强,构建语义超图。经过超图卷积后通过注意力机制对双超图特征进行融合,实现短文本分类。在4个文本分类数据集上的实验结果表明,该模型优于基线模型,具有优越的文本分类性能。

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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: 2025-09-16 15:59:47.197087
original_link:
  https://www.cqvip.com/doc/journal/3500943458?sign=ec3a043a9bd88c4c9b17db496b7daff52cafca47df0af56328be7a1ccb45b31b&expireTime=1789545494755&resourceId=3500943458

summary:
  随着互联网技术的发展,产生了大量的各种背景下的文本信息,包括新闻资料,科学文献等等,这些文本信息所包含的内容并不统一.对于神经网络而言,不断地增加新领域的训练数据必然会导致对旧信息的灾难遗忘.因此在实际应用中,训练出可以终身学习的文本分类器具有很高的应用价值.为此,该文提出了一种基于网络剪枝和动态扩展策略的终身文本分类方法,适用于知识背景不断增加的文本分类场景.具体而言,该文将网络剪枝、参数正则化更新和网络扩展相结合,保证神经网络可以在不遗忘旧任务知识的前提下更有效的学习新任务.通过使用不确定正则化参数更新策略防止旧领域信息的遗忘,实现任务之间的知识迁移.在5个流行的文本分类数据集上进行终身学习的实验设置下进行验证,实验结果表明本文的方法与BERT预训练模型相比高出约10%,并且均优于其他终身学习的方法.

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