# api_taskdetail

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 15:59:47.197565
original_link:
  https://www.cqvip.com/doc/journal/3502552353?sign=271a24287163731cce557489590f32909bf18c90db3932a249445054e77dc402&expireTime=1789545494755&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 15:59:47.198035
original_link:
  https://www.cqvip.com/doc/journal/3501769600?sign=d3bb70fa00ba34fc85b042c15e5549ed0dd43ee2f1e8e563a3dc3c3993821d0c&expireTime=1789545494755&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 15:59:47.198541
original_link:
  https://www.cqvip.com/doc/journal/3501556210?sign=ca4178562c8c4dc699b30d2f1f0507729d8ae5e980ac2279bdd092bb8990cff5&expireTime=1789545494755&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 15:59:47.199020
original_link:
  https://www.cqvip.com/doc/journal/3483736790?sign=27a7ddeba450fe7e7f116e3d7fb7d10cdc6fbeec2b72572160b5297a366a8328&expireTime=1789545494755&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 15:59:47.199493
original_link:
  https://www.cqvip.com/doc/journal/3483995749?sign=8f4a241c7787342fee46c0263e1c481e567cc2d647e6600ae4768938e70eb605&expireTime=1789545494755&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: 周仲礼;柳炳利;龚成杰;曹昌杰;孔韫辉
date: 2025-09-15 16:45
keywords: 矿产勘查;;知识图谱;;图神经网络;;本体建模;;知识推理;
source: 成都理工大学学报(自然科学版)
site: 知网
title: 矿产勘查知识图谱——研究进展、关键问题与未来展望
created_at: 2025-09-17 12:52:37.654816
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlM1-uo8NldNyaoLQiOlslLCRvHNhAAodXdOZOkF7cdmIygyuD3r5FCbPLsBVD34Ag0O-C1MDHgKlOk1jb5tCUN531vBnfi2DbHBSd2ylFKdKjcvwxzH2_esxV11ZQSsYbBs34VP02a4LCAcBzfqBXwiwVHlgpM2HplCDzz1BOJVkg==&uniplatform=NZKPT&language=CHS

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

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author: 肖克江;陈亮;方铄;庞世燕;邱杰凡
date: 2025-09-10 16:14
keywords: 在线课程;;推荐系统;;知识图谱;;图注意力网络;;特征交叉;
source: 数据分析与知识发现
site: 知网
title: 融合课程知识图谱与图注意力网络的推荐模型研究
created_at: 2025-09-17 12:52:37.657948
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlM8ol8WgTx5KEJarT3p2Gx0kYW6nCBShWw3_Chlchb8ownat2dA9voT2zMgi9M6B1-80O5zptgO72HS3CqaNmNRLevH01oNoVBCwgZrhuVy-JOFHjdPFK9d_bMLwPqU2awcDMFFs-HoKIFUQEHaB_z53j9m9H5NAcYSHvEp02H_jg==&uniplatform=NZKPT&language=CHS

summary:
  [目的]针对在线课程推荐存在数据稀疏、冷启动和特征利用不充分的问题，本文提出了一种融合课程知识图谱与图注意力网络的推荐模型（CKGAT）。[方法]CKGAT的学习者模块通过特征交叉层和全连接层分别提升学习者特征的记忆和泛化能力，课程模块基于图注意力网络挖掘课程实体间的高阶语义特征。本文对两个模块的输出向量进行点积计算后，得到推荐结果。[结果]在基于MoocCubeX数据集的对比实验中，CKGAT在ACC、F1和AUC指标上相较于最好的基线模型分别提升了1.28%、1.62%和1.00%。[局限]本文的课程知识图谱略显单薄，模型的计算复杂度可进一步优化。[结论]本文提出的CKGAT取得了较好的推荐结果，有助于提升在线课程推荐效果。

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author: 敖若瑶
date: 2025-09-09
keywords: 知识图谱;;文化遗产;;数字人文;;大模型;
source: 科技与创新
site: 知网
title: 文化遗产领域知识图谱发展趋势与前沿进展研究
created_at: 2025-09-17 12:52:37.660256
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlM8ol8WgTx5KEJarT3p2Gx0K1jBDnFb-Omxsex6_H1ExvphHmYkIl8zpIuRzaCuS0T3OS3DwZWvh-3-eQlsEkkLd_W8NZ8iegNxeYB9aZlTOpzI1MB3J8QLSq1ryQD9bGNKduhxi9a5FeV3vTk6Z33l1O4SHWFSCOJejKbAy8QX8A==&uniplatform=NZKPT&language=CHS

summary:
  知识图谱作为学界较为成熟的一项技术，为分散且多维度的文化遗产资源提供了结构化与语义化整合的解决方案。运用文献计量法和文献分析法，借助CiteSpace软件对从CNKI期刊库中获取的共计92篇相关文献进行科学知识图谱可视化分析，旨在揭示文化遗产领域知识图谱研究的演进脉络、热点主题以及前沿趋势。研究总结归纳出文化领域知识图谱研究的演进可划分为3个阶段，即萌芽期（2015—2019年）、发展期（2020—2023年）、探索期（2024—2025年），发现大模型与智能体技术显著推动了文化遗产知识图谱在知识生成、多模态交互以及动态服务方面的革新。展望未来，可以重点关注多模态知识图谱的深度整合与动态表达，以及智能体驱动的主动化知识服务系统等方向，以深化文化遗产的智慧化保护与传播。

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author: 王菁;刘丹
date: 2025-09-02
keywords: 思政课;;知识图谱;;精准教学;
source: 思想理论教育
site: 知网
title: 基于知识图谱的思政课精准教学探析
created_at: 2025-09-17 12:52:37.662300
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlM8ol8WgTx5KEJarT3p2Gx0K1jBDnFb-Okv_sNnwwvY9usxV3ILGwBW_5DHbfZKbZ1yxfxk0ZxHkAFKx5dCUYtcqiAti1-tX9ENGlbUjmoUot-52JiIAYV0tM3jC1jhKPy3L-8O3n2kYWBZEIiIyyXRmyILIIr8HJKj786LdeF6Cg==&uniplatform=NZKPT&language=CHS

summary:
  运用知识图谱技术构建思政课知识图谱，是推动思政课精准教学的必要举措。知识图谱赋能高校思政课精准教学就在于其在推动教学内容的贯通性与针对性、把握教育对象的群体性与个体性、促进教学全过程和全要素评价等方面具有重要价值。基于此，应构建框架与流程，打造全面、系统且可交互的思政课知识图谱，并从精准描摹画像、高效利用资源、评估实时学情等方面积极探索实施路径，以此实现个性化学习路径推荐、针对性教学内容供给、教与学成效即时反馈，从而提高思政课教学的针对性和实效性。

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author: 张宝一;唐嘉成;张彤蕴;王宾海;史与正
date: 2025-09-01 10:48
keywords: 地质找矿;;知识图谱;;大语言模型;;检索增强生成;;地质本体;;思维链;
source: 地球科学
site: 知网
title: 大语言模型赋能的地质找矿知识图谱与问答模型构建
created_at: 2025-09-17 12:52:37.663870
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPl5naU6BpqkOPzsO8d4ul-zCdtYWVboZONMmrxmFh1S2NBitx_rKZ8zDMk8B9Vr8LVJslnsYAAZV0a03oz2NTOM3r2ssVW7h2EBnTYEN5YWsNSw4RBLWp51uKwcJAoAxFL7_7YqtAzYmCIDF1GtMmIxJrUCffYnb5ByMrV-6UfHw==&uniplatform=NZKPT&language=CHS

summary:
  当前地质找矿领域的大语言模型应用面临着专业知识不足、数据隐私安全和模型幻觉等问题，同时大语言模型在地质找矿领域应用中仍缺乏高效快捷的知识推荐手段。本研究提出了知识图谱与检索增强生成相结合的KG-RAG (Knowledge graph Retrieval-Augmented Generation) 框架，以大语言模型为工具，在地质本体约束下实现了地质找矿知识图谱的自动化抽取和结构化表达，同时利用知识图谱的多跳检索算法实现检索内容的深度与广度优化，实现了地质找矿智能知识问答模型。实验结果表明：KG-RAG在准确率、召回率和可信度(F1-score)上分别取得的0.807、0.833和0.819，在知识图谱构建任务相比大语言基模型GLM4-9B的直接知识抽取，分别取得了约50%、8%和29%的提升；在问答任务中，KG-RAG召回率和准确率上分别取得了0.917和0.88，相比文档向量检索增强生成方法分别取得了约24%和22%的提升。KG-RAG在知识图谱构建与智能问答两方面均表现出了较好的性能，能够有效从地质资料中进行地质找矿知识收集与表达，支持地质工作者的地质调查与找矿预测工作，本研究为...

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author: 敖若瑶
date: 2025-08-25
keywords: 知识图谱;;人工智能;;大模型;;向量数据库;
source: 现代信息科技
site: 知网
title: 知识图谱演进与新兴技术融合趋势研究
created_at: 2025-09-17 12:52:37.665202
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPl5naU6BpqkOPzsO8d4ul-qJeBPkzOYRo3bG-fF0gWBnDqlP71oR0fEz8_mLy3U4ss8HIq85cwJS1eRsz0BcMrKmmvszClsCDU185KAjOsj1SztH3PU3Sf7014CKezG4FS_xsFB_e0QYeLdfxMn4QXdlU9eKb-wfx5uO28aDzi4w==&uniplatform=NZKPT&language=CHS

summary:
  文章系统梳理了知识图谱的演变进展及其与大模型、向量数据库、智能体新兴技术的融合研究进展，旨在为未来知识图谱的发展提供理论框架与实践指引。首先通过从多维视角讨论知识图谱的演变，包括应用范围、知识变化特性和数据表现形式。随后探讨了其与大模型、向量数据库和智能体的前沿交互进展。知识图谱的发展展现了从一般到垂直、从静态到动态、从单一到多元的转变。未来的知识图谱研究应更加注重动态知识图谱的构建、多模态大模型的融合以及与新兴技术的交互实现，以适应人工智能和大数据时代的需求。

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author: 梁娜;张倩瑜;张晓庆;周超;蔡婷婷
date: 2025-08-25
keywords: 护理本科生;;知识图谱;;BOPPPS模式;;围手术期护理;;外科护理学;;课程思政;;自主学习能力;;护理教育;
source: 护理学杂志
site: 知网
title: 基于知识图谱的BOPPPS模式在围手术期护理教学中的应用
created_at: 2025-09-17 12:52:37.666350
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPl5naU6BpqkOPzsO8d4ul-qJeBPkzOYRqYuRcdffPXi7Oc5q5xPO-hC9XREbbiNTnXE0WuP5tlZijm9SV9eg-nb56KAnndRKAupQ-FmGJYxP1ta87wQSoeU26A9gdm4Bvoi4fhoAwjhoB6SJCdP7ANm1-_0Dux-s99MN9KoNz07w==&uniplatform=NZKPT&language=CHS

summary:
  目的 构建并评估基于知识图谱的BOPPPS模式在围手术期护理教学的应用效果。方法 便利抽取2022级4个护理本科班级，按班级随机分为对照组和试验组各79人。对照组实施常规围手术期护理教学，试验组依托超星泛雅平台的知识图谱技术，构建围手术期护理知识图谱体系，包括知识图谱、问题图谱和课程思政图谱，实施基于知识图谱的BOPPPS教学。课程结束后比较两组考核成绩与自主学习能力，并结合质性访谈分析知识图谱的使用体验与效果。结果 对照组78人、试验组79人完成研究。课程结束后，试验组理论成绩、总成绩和自主学习能力评分显著高于对照组(均P<0.05),两组操作成绩差异无统计学意义(P>0.05)。访谈共提炼5个主题，知识图谱教学能提升学习获得感与使用意愿，促进知识理解与高效记忆等。结论 基于知识图谱的BOPPPS模式在围手术期护理教学中的应用，可有效提升护理本科生学习成绩与自主学习能力，强化知识整合与职业认同。

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author: 段一凡;刘然;刘小杰;张玉洁;靳亚涛
date: 2025-08-21 19:18
keywords: 强化冶炼;;高炉专家系统;;知识推理;;知识图谱;;语义网络;
source: 中国冶金
site: 知网
title: 知识图谱在高炉专家系统中的应用前景
created_at: 2025-09-17 12:52:37.667300
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNfzjBEbsAoi1v4Hd0nhTw30o1UQYCw_yBinjfRyfwQufHxgFLDyQnL8qpxx4P_RyWxgw2dEagSxkt_kXnkDX0BDa_l7eFqQIbNGdTut-mQW77gD3_NxoifuNcX6pLK7B48ZScnqMen2Z-9JeUi2k4h-dvrtlz-WKR0i6jtkN2Iuw==&uniplatform=NZKPT&language=CHS

summary:
  受限于知识推理模式的局限性，中国传统高炉专家系统在日益复杂的工况下逐渐表现出劣势，难以实现跨高炉场景的大规模推广。知识图谱是一种基于图结构数据的新型信息管理检索技术。首先，梳理了中国传统高炉专家系统的搭建机制，包括基于规则与逻辑、基于典型案例以及基于冶金数学模型3种知识推理模式，并深入分析了当前面临的痛点问题。其次，探索了高炉大数据背景下的知识图谱构建方法，并以先前工作为基础构建了以铁水钒含量为目标节点的知识图谱网络示例，绘制了相关的参数调控富集网络，验证了知识图谱在解耦多参数关联作用方面的优势。最后，总结了知识图谱技术在高炉专家系统中的应用方向，包括但不限于多模态数据的检索与管理、多参数关联作用的解耦、工艺的动态调控与优化、炉况的智能诊断、智能化决策的动态生成与推荐5个方面。知识图谱能够有效解决传统高炉专家系统规则复杂度高、单向检索、自适应能力不足等问题，促进传统基于规则的参数调控方法向全方位数据驱动的智能决策转变。知识图谱技术的高效利用是未来高炉专家系统转型与升级的重要方向。

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author: 刘启恒;李雪莲;孙孟娜
date: 2025-08-20
keywords: 知识图谱;;CiteSpace与SciExplorer-LDGAS;;文献计量分析;;网络安全;;网络空间命运共同体;;人工智能技术;
source: 南京师大学报(自然科学版)
site: 知网
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: 2025-09-17 12:52:37.668168
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNfzjBEbsAoi1v4Hd0nhTw3HvRvkfBmLl03jN98ucStdw58sWbpKgz1Ub2Ot1hk9LL1XW1RcI13UMJEeiONG0QbFSjta4I5lL8WIiztMl53rauQOKttRCqduZUzdEJ9AsFoD-iRovjQvHHlSzsYZlRvbKPY560iM8xdAzZC5pASpg==&uniplatform=NZKPT&language=CHS

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...

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author: 商惠华
date: 2025-08-20
keywords: 知识图谱;;MOOC;;可视化;;计算机科学与技术(师范)专业课程;
source: 数字通信世界
site: 知网
title: 基于MOOC平台和知识图谱的专业学习规划研究
created_at: 2025-09-17 12:52:37.668940
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlN_nZoz3jujgoNzOIqs3RP8hYg-HzYwoq0_hxgtBInk5Ht-ub2XWdVFanXLwduk3ePcYU2rmVQJjn2eWfOvW2YiJZ43DQI2wXoflhQ_F6w7iAxk8ZL_OEzUET4fHYhxlpgSeHbjjVxdXxLGfoy52n7W7L3PIHHenNz04WJMrbhm7w==&uniplatform=NZKPT&language=CHS

summary:
  在国家倡导“数字化转型，推动教育高质量发展”的背景下，知识图谱的巨大潜力在教育领域得以彰显。本研究以计算机科学与技术(师范)专业为例，整合MOOC丰富教学资源，深入剖析专业课程架构与学习需求，构建专业知识图谱和课程知识图谱，并借助码投图谱数据库实现可视化呈现。通过该图谱，学习者能从宏观把握专业课程框架，从微观获取具体课程的MOOC学习资源。为检验其应用效果，对相关专业学生开展问卷调查，结果显示知识图谱在辅助学生课外自主学习、引导学习方向、提升学习效率方面成效显著。本研究为教育领域应用知识图谱优化学习规划提供了实践参考，也为后续研究奠定了基础。

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author: 周思俊;张昊;王国明
date: 2025年19期
keywords: 知识图谱;知识图谱构建;知识推理;深度学习;应用框架
quote: 0
source: 《电脑知识与技术》
site: 万方
title: 知识图谱研究综述
created_at: 2025-09-17 12:52:37.669608
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISGWRuenN5anMtaXRyenlrc2IyMDI1MTkwMTAaCDQ3dm9rOWR6

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

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author: 李雯昕;昝红英;关同峰
date: 2025年3期
keywords: 知识图谱;命名实体识别;关系抽取;期货文本
quote: 0
source: 《中文信息学报》
site: 万方
title: 期货领域知识图谱构建
created_at: 2025-09-17 12:52:37.670200
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p3eHh4YjIwMjUwMzAwMhoINDd2b2s5ZHo%3D

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

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author: 石栖;陈文杰;胡正银
date: 2025年3期
keywords: 科学实验;事件知识图谱;知识发现;大语言模型
quote: 2
source: 《数据分析与知识发现》
site: 万方
title: 面向知识发现的科学实验知识图谱构建研究
created_at: 2025-09-17 12:52:37.670778
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAzMDAxGgg0N3Zvazlkeg%3D%3D

summary:
  [目的]从科技文献中高效挖掘科学实验知识与数据,构建科学实验知识图谱,为知识发现提供高质量数据支撑.[方法]利用事件知识图谱技术对科学实验的复杂性、时序性及知识和数据融合性等知识对象进行统一的知识表示与建模,构建科学实验知识图谱模式层;利用大语言模型提升科学实验知识图谱数据层的知识抽取效率,并以有机太阳能电池为例进行实证.[结果]采用人工标注与大语言模型微调方式构建了一个有机太阳能电池领域科学实验知识图谱,包含34类节点,9种关系,总计有24 348个节点和123 642个关系.[局限]数据来源仅包括论文和专利;科学实验知识图谱构建需要较多专家人工参与,效率尚待进一步提高;未考虑细分领域中的细粒度研究规程和研究方法检验规则等.[结论]本文提出的学科领域科学实验知识图谱构建方法可为实验方案推荐、科学实验演化分析、AI for Science等提供高质量数据支持,有效支撑各类知识发现场景.

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author: 陈强;张栋;李寿山
date: 2025年4期
keywords: 知识图谱补全;多模态;知识融合;多模态融合
quote: 0
source: 《软件学报》
site: 万方
title: 融合任务知识的多模态知识图谱补全
created_at: 2025-09-17 12:52:37.671283
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDQwMDgaCDQ3dm9rOWR6

summary:
  知识图谱补全任务旨在根据已有的事实三元组(头实体、关系、尾实体)来挖掘知识图谱中缺失的事实三元组.现有的研究工作主要致力于利用知识图谱中的结构信息来进行知识图谱补全任务.然而,这些工作忽略了知识图谱中蕴含的其他模态的信息也可能对知识图谱补全有帮助.并且,由于基于特定任务的知识通常没有被注入通用的预训练模型,因而如何在抽取模态信息的过程中融合任务的相关知识变得至关重要.此外,因为不同模态特征对于知识图谱补全的贡献不一样,所以如何有效地保留有用的多模态信息也是一大挑战.为了解决上述问题,提出一种融合任务知识的多模态知识图谱补全方法.利用在当前任务上微调过的多模态编码器,来获取不同模态下的实体向量表示.并且,通过一个基于循环神经网络的模态融合过滤模块,去除与任务无关的多模态特征.最后,利用同构图网络表征并更新所有特征,从而有效地完成多模态知识图谱补全任务.实验结果表明,所提出的方法能有效地抽取不同模态的信息,并且能够通过进一步的多模态过滤融合来增强实体的表征能力,进而提高多模态知识图谱补全任务的性能.

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author: 赵珂;段林伶;李洁莲
date: 2025年2期
keywords: DIKW模型;知识图谱技术;智慧城市规划;知识表示;知识计算;可解释性推理
quote: 0
source: 《国际城市规划》
site: 万方
title: 知识图谱技术支持
created_at: 2025-09-17 12:52:37.671775
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2d3Y3NnaDIwMjUwMjAxNBoINDd2b2s5ZHo%3D

summary:
  以大数据、机器学习应用为主的智慧城市规划仅限于描述城市现象和表象问题是什么、怎么样以及"黑箱"式计算,无法揭示方案生成原因(为什么).受"数据—信息—知识—智慧"金字塔层次DIKW模型的启示,本文引入知识图谱技术,剖析其中知识表示和知识计算两大核心技术对城市规划智慧生成的原理和决策方式的引导,总结出由"建设基础本体集—建立规划方案推理本体—智慧推演规划方案—智慧决策支持规划调整"构成的全生命周期智慧城市规划的知识图谱技术方法和推进路径,赋予城市规划具有"知道为什么"的智慧决策支持能力.

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author: 陆海洋;柳先辉;侯文龙
date: 2025年3期
keywords: 负采样;知识图谱;推荐系统;正混合
quote: 0
source: 《计算机科学》
site: 万方
title: 融合知识图谱的负采样方法
created_at: 2025-09-17 12:52:37.672234
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzamt4MjAyNTAzMDE5Ggg0N3Zvazlkeg%3D%3D

summary:
  为了解决信息过载的问题,推荐系统被广泛研究.由于很难获取大量高质量的显式反馈数据,隐式反馈数据成为训练推荐系统的主流选择.从未标记的数据中采样负例,即负采样,对于训练基于隐式反馈的推荐模型非常重要.现有推荐系统的负采样方法往往只关注如何选择包含更多用户偏好信息的强负样例,却没有考虑强负样例的假阴性问题.为了降低采样得到的负样例的假阴性概率并提高其信息量,提出了一种融合知识图谱的负采样方法.首先,根据用户-项目知识图谱构建负样例候选集;然后,通过基于贝叶斯分类的方式从候选集中筛选假阴性概率最小的负样例;最后,基于Mixup策略引入正混合技术构建强负样例.为了验证所提出方法的有效性,在两个公开数据集上进行了实验.结果表明,与现有方法相比,所提方法表现更优.

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author: 叶志鸿;吴运兵;戴思翀
date: 2025年3期
keywords: 知识图谱补全;多模态融合;本体信息;结构信息;决策学习
quote: 0
source: 《计算机科学与探索》
site: 万方
title: 多级融合知识图谱补全模型
created_at: 2025-09-17 12:52:37.672709
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWpzamt4eXRzMjAyNTAzMDEzGgg0N3Zvazlkeg%3D%3D

summary:
  知识图谱补全旨在通过预测缺失的三元组来扩展和完善知识图谱,多模态知识图谱补全融合了实体的本体信息,如实体描述、实体图像和实体属性,以获取更精确的实体表示.现有研究将不同模态投影到统一的空间中,以获取实体模态联合表示,再融合知识图谱结构信息作出预测.然而,现存方法融合多模态信息时难以捕捉实体背景知识的复杂交互,不可避免地存在信息丢失和特征提取能力不足的问题;同时过拟合及实体关系交互不足限制了二维卷积模型性能,导致难以融合知识图谱结构信息.因此,提出了多级融合知识图谱补全模型,从实体多模态信息融合与知识图谱结构信息融合两方面解决上述问题.为充分融合实体多模态信息,提出同时使用三种不同融合方法,以全面捕捉实体背景知识交互,并联合决策学习,旨在结合不同多模态融合方法提供的互补信息,以获取实体丰富多样的表示;为充分融合知识图谱结构信息,利用特征泛化来缓解二维卷积模型的过拟合问题,并结合特征重塑增强实体与关系间交互,以提升实体与关系间的上下文感知能力.实验结果表明,该模型在多个公开数据集上均取得较好性能.

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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: 2025-09-17 12:52:37.673200
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAxMDA3Ggg0N3Zvazlkeg%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: 2025-09-17 12:52:37.673658
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hkeHhrajIwMjUwODAyMxoINDd2b2s5ZHo%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: 2025-09-17 12:52:37.674114
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA2MDA5Ggg0N3Zvazlkeg%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: Zhihao He;Tianyao He;Tieyuan Chen;Yun Xu;Huabin Liu;Chaofan Gan;Gui Zou;Weiyao Lin
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.13161
pdf_url: https://arxiv.org/pdf/2509.13161
site: arxiv
created_at: 2025-09-17 12:52:37.674586
summary:
  Abstract:Despite the prosperity of the video language model, the current pursuit of comprehensive video reasoning is thwarted by the inherent spatio-temporal incompleteness within individual videos, resulting in hallucinations and inaccuracies. A promising solution is to augment the reasoning performance with multiple related videos. However, video tokens are numerous and contain redundant information, so directly feeding the relevant video data into a large language model to enhance responses could be counterproductive. To address this challenge, we propose a multi-video collaborative framework for video language models. For efficient and flexible video representation, we establish a Video Structuring Module to represent the video's knowledge as a spatio-temporal graph. Based on the structured video representation, we design the Graph Fusion Module to fuse the structured knowledge and valuable information from related videos into the augmented graph node tokens. Finally, we construct an elaborate multi-video structured prompt to integrate the graph, visual, and textual tokens as the input to the large language model. Extensive experiments substantiate the effectiveness of our framework, showcasing its potential as a promising avenue for advancing video language models.

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


author: Francesco Zola;Jon Ander Medina;Andrea Venturi;Amaia Gil;Raul Orduna
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12730
pdf_url: https://arxiv.org/pdf/2509.12730
site: arxiv
title: A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs
created_at: 2025-09-17 12:52:37.675030
summary:
  Abstract:The rise of digital ecosystems has exposed the financial sector to evolving abuse and criminal tactics that share operational knowledge and techniques both within and across different environments (fiat-based, crypto-assets, etc.). Traditional rule-based systems lack the adaptability needed to detect sophisticated or coordinated criminal behaviors (patterns), highlighting the need for strategies that analyze actors' interactions to uncover suspicious activities and extract their modus operandi. For this reason, in this work, we propose an approach that integrates graph machine learning and network analysis to improve the detection of well-known topological patterns within transactional graphs. However, a key challenge lies in the limitations of traditional financial datasets, which often provide sparse, unlabeled information that is difficult to use for graph-based pattern analysis. Therefore, we firstly propose a four-step preprocessing framework that involves (i) extracting graph structures, (ii) considering data temporality to manage large node sets, (iii) detecting communities within, and (iv) applying automatic labeling strategies to generate weak ground-truth labels. Then, once the data is processed, Graph Autoencoders are implemented to distinguish among the well-known topological patterns. Specifically, three different GAE variants are implemented and compared in this analysis. Preliminary results show that this pattern-focused, topology-driven method is effective for detecting complex financial crime schemes, offering a promising alternative to conventional rule-based detection systems.

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author: Jie Yin;Ke Sun;Han Wu
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12727
pdf_url: https://arxiv.org/pdf/2509.12727
site: arxiv
title: Unbiased Online Curvature Approximation for Regularized Graph Continual Learning
created_at: 2025-09-17 12:52:37.675513
summary:
  Abstract:Graph continual learning (GCL) aims to learn from a continuous sequence of graph-based tasks. Regularization methods are vital for preventing catastrophic forgetting in GCL, particularly in the challenging replay-free, class-incremental setting, where each task consists of a set of unique classes. In this work, we first establish a general regularization framework for GCL based on the curved parameter space induced by the Fisher information matrix (FIM). We show that the dominant Elastic Weight Consolidation (EWC) and its variants are a special case within this framework, using a diagonal approximation of the empirical FIM based on parameters from previous tasks. To overcome their limitations, we propose a new unbiased online curvature approximation of the full FIM based on the model's current learning state. Our method directly estimates the regularization term in an online manner without explicitly evaluating and storing the FIM itself. This enables the model to better capture the loss landscape during learning new tasks while retaining the knowledge learned from previous tasks. Extensive experiments on three graph datasets demonstrate that our method significantly outperforms existing regularization-based methods, achieving a superior trade-off between stability (retaining old knowledge) and plasticity (acquiring new knowledge).

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author: Ruizhong Qiu;Ting-Wei Li;Gaotang Li;Hanghang Tong
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12530
pdf_url: https://arxiv.org/pdf/2509.12530
site: arxiv
title: Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs
created_at: 2025-09-17 12:52:37.675964
summary:
  Abstract:Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph-structured data. However, existing GNNs often struggle with heterophilic graphs, where connected nodes tend to have dissimilar features or labels. While numerous methods have been proposed to address this challenge, they primarily focus on architectural designs without directly targeting the root cause of the heterophily problem. These approaches still perform even worse than the simplest MLPs on challenging heterophilic datasets. For instance, our experiments show that 21 latest GNNs still fall behind the MLP on the Actor dataset. This critical challenge calls for an innovative approach to addressing graph heterophily beyond architectural designs. To bridge this gap, we propose and study a new and unexplored paradigm: directly increasing the graph homophily via a carefully designed graph transformation. In this work, we present a simple yet effective framework called GRAPHITE to address graph heterophily. To the best of our knowledge, this work is the first method that explicitly transforms the graph to directly improve the graph homophily. Stemmed from the exact definition of homophily, our proposed GRAPHITE creates feature nodes to facilitate homophilic message passing between nodes that share similar features. Furthermore, we both theoretically and empirically show that our proposed GRAPHITE significantly increases the homophily of originally heterophilic graphs, with only a slight increase in the graph size. Extensive experiments on challenging datasets demonstrate that our proposed GRAPHITE significantly outperforms state-of-the-art methods on heterophilic graphs while achieving comparable accuracy with state-of-the-art methods on homophilic graphs.

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author: Evan Sundbo
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12368
pdf_url: https://arxiv.org/pdf/2509.12368
site: arxiv
title: Balloon Animal Maps with Applications to the Cohomology of Hypertoric Hitchin Systems
created_at: 2025-09-17 12:52:37.676426
summary:
  Abstract:In this article we introduce the notion of a balloon animal map between broken toric varieties and construct several long exact sequences in cohomology related to them. We give a new proof of the deletion-contraction relation on hypertoric Hitchin systems of Dansco-Mcbreen-Shende and present some refinements of it. The end result is a formula for the Poincaré polynomial of any hypertoric Hitchin system associated to a graph with first Betti number 2 along with a recipe to calculate the Poincaré polynomial of any hypertoric Hitchin system, given knowledge of a finite number of base cases.

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author: Ke Sun;Mayi Xu
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12350
pdf_url: https://arxiv.org/pdf/2509.12350
site: arxiv
title: Knowledge Graph Tokenization for Behavior-Aware Generative Next POI Recommendation
created_at: 2025-09-17 12:52:37.676890
summary:
  Abstract:Generative paradigm, especially powered by Large Language Models (LLMs), has emerged as a new solution to the next point-of-interest (POI) recommendation. Pioneering studies usually adopt a two-stage pipeline, starting with a tokenizer converting POIs into discrete identifiers that can be processed by LLMs, followed by POI behavior prediction tasks to instruction-tune LLM for next POI recommendation. Despite of remarkable progress, they still face two limitations: (1) existing tokenizers struggle to encode heterogeneous signals in the recommendation data, suffering from information loss issue, and (2) previous instruction-tuning tasks only focus on users' POI visit behavior while ignore other behavior types, resulting in insufficient understanding of mobility. To address these limitations, we propose KGTB (Knowledge Graph Tokenization for Behavior-aware generative next POI recommendation). Specifically, KGTB organizes the recommendation data in a knowledge graph (KG) format, of which the structure can seamlessly preserve the heterogeneous information. Then, a KG-based tokenizer is developed to quantize each node into an individual structural ID. This process is supervised by the KG's structure, thus reducing the loss of heterogeneous information. Using generated IDs, KGTB proposes multi-behavior learning that introduces multiple behavior-specific prediction tasks for LLM fine-tuning, e.g., POI, category, and region visit behaviors. Learning on these behavior tasks provides LLMs with comprehensive insights on the target POI visit behavior. Experiments on four real-world city datasets demonstrate the superior performance of KGTB.

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date: 14 September, 2025
original_link: https://arxiv.org/abs/2509.12277
pdf_url: https://arxiv.org/pdf/2509.12277
site: arxiv
created_at: 2025-09-17 12:52:37.677340
author:
  Mehdi Yousefzadeh;Parsa Esfahanian;Sara Rashidifar;Hossein Salahshoor Gavalan;Negar Sadat Rafiee Tabatabaee;Saeid Gorgin;Dara Rahmati;Maryam Daneshpazhooh

summary:
  Abstract:Introduction. Dermoscopy aids melanoma triage, yet image-only AI often ignores patient metadata (age, sex, site) and the physical scale needed for geometric analysis. We present GraphDerm, a population-graph framework that fuses imaging, millimeter-scale calibration, and metadata for multiclass dermoscopic classification, to the best of our knowledge the first ISIC-scale application of GNNs to dermoscopy. Methods. We curate ISIC 2018/2019, synthesize ruler-embedded images with exact masks, and train U-Nets (SE-ResNet-18) for lesion and ruler segmentation. Pixels-per-millimeter are regressed from the ruler-mask two-point correlation via a lightweight 1D-CNN. From lesion masks we compute real-scale descriptors (area, perimeter, radius of gyration). Node features use EfficientNet-B3; edges encode metadata/geometry similarity (fully weighted or thresholded). A spectral GNN performs semi-supervised node classification; an image-only ANN is the baseline. Results. Ruler and lesion segmentation reach Dice 0.904 and 0.908; scale regression attains MAE 1.5 px (RMSE 6.6). The graph attains AUC 0.9812, with a thresholded variant using about 25% of edges preserving AUC 0.9788 (vs. 0.9440 for the image-only baseline); per-class AUCs typically fall in the 0.97-0.99 range. Conclusion. Unifying calibrated scale, lesion geometry, and metadata in a population graph yields substantial gains over image-only pipelines on ISIC-2019. Sparser graphs retain near-optimal accuracy, suggesting efficient deployment. Scale-aware, graph-based AI is a promising direction for dermoscopic decision support; future work will refine learned edge semantics and evaluate on broader curated benchmarks.

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title:
  GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions


author: Tianqi Zhao;Russa Biswas;Megha Khosla
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12094
pdf_url: https://arxiv.org/pdf/2509.12094
site: arxiv
title: Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
created_at: 2025-09-17 12:52:37.677811
summary:
  Abstract:Graph machine learning models often achieve similar overall performance yet behave differently at the node level, failing on different subsets of nodes with varying reliability. Standard evaluation metrics such as accuracy obscure these fine grained differences, making it difficult to diagnose when and where models fail. We introduce NodePro, a node profiling framework that enables fine-grained diagnosis of model behavior by assigning interpretable profile scores to individual nodes. These scores combine data-centric signals, such as feature dissimilarity, label uncertainty, and structural ambiguity, with model-centric measures of prediction confidence and consistency during training. By aligning model behavior with these profiles, NodePro reveals systematic differences between models, even when aggregate metrics are indistinguishable. We show that node profiles generalize to unseen nodes, supporting prediction reliability without ground-truth labels. Finally, we demonstrate the utility of NodePro in identifying semantically inconsistent or corrupted nodes in a structured knowledge graph, illustrating its effectiveness in real-world settings.

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author: Haodong Chen;Haojian Huang;XinXiang Yin;Dian Shao
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.11796
pdf_url: https://arxiv.org/pdf/2509.11796
site: arxiv
title: FineQuest: Adaptive Knowledge-Assisted Sports Video Understanding via Agent-of-Thoughts Reasoning
created_at: 2025-09-17 12:52:37.678259
summary:
  Abstract:Video Question Answering (VideoQA) based on Large Language Models (LLMs) has shown potential in general video understanding but faces significant challenges when applied to the inherently complex domain of sports videos. In this work, we propose FineQuest, the first training-free framework that leverages dual-mode reasoning inspired by cognitive science: i) Reactive Reasoning for straightforward sports queries and ii) Deliberative Reasoning for more complex ones. To bridge the knowledge gap between general-purpose models and domain-specific sports understanding, FineQuest incorporates SSGraph, a multimodal sports knowledge scene graph spanning nine sports, which encodes both visual instances and domain-specific terminology to enhance reasoning accuracy. Furthermore, we introduce two new sports VideoQA benchmarks, Gym-QA and Diving-QA, derived from the FineGym and FineDiving datasets, enabling diverse and comprehensive evaluation. FineQuest achieves state-of-the-art performance on these benchmarks as well as the existing SPORTU dataset, while maintains strong general VideoQA capabilities.

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author: Samir Moustafa;Lorenz Kummer;Simon Fetzel;Nils M. Kriege;Wilfried N. Gansterer
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.11792
pdf_url: https://arxiv.org/pdf/2509.11792
site: arxiv
title: Visualization and Analysis of the Loss Landscape in Graph Neural Networks
created_at: 2025-09-17 12:52:37.678695
summary:
  Abstract:Graph Neural Networks (GNNs) are powerful models for graph-structured data, with broad applications. However, the interplay between GNN parameter optimization, expressivity, and generalization remains poorly understood. We address this by introducing an efficient learnable dimensionality reduction method for visualizing GNN loss landscapes, and by analyzing the effects of over-smoothing, jumping knowledge, quantization, sparsification, and preconditioner on GNN optimization. Our learnable projection method surpasses the state-of-the-art PCA-based approach, enabling accurate reconstruction of high-dimensional parameters with lower memory usage. We further show that architecture, sparsification, and optimizer's preconditioning significantly impact the GNN optimization landscape and their training process and final prediction performance. These insights contribute to developing more efficient designs of GNN architectures and training strategies.

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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: 2025-09-17 12:52:37.681884
original_link:
  https://www.cqvip.com/doc/journal/3508551509?sign=e84f94c320c9762a285a32c1d5fbc80c1aac6bb687fa5303ec14160a35772033&expireTime=1789620663828&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
keywords: 绿色就业;CiteSpace;演化分析;高质量发展;知识群组
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: 2025-09-17 12:52:37.682354
original_link:
  https://www.cqvip.com/doc/journal/3506292255?sign=66676f2c85821cd7ed7be2f03e6c72d98ebc8c895f51678594016e87a6944247&expireTime=1789620663828&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
keywords: IFC;TransE模型;知识图谱;语义信息提取
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: 2025-09-17 12:52:37.682809
original_link:
  https://www.cqvip.com/doc/journal/3506618902?sign=5866d994125f3f9768ff192772fcd15d3ff4d970da78013829623b7e39971ce9&expireTime=1789620663828&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
keywords: 教材编制;模型构建;国际中文数智教材;知识图谱
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: 2025-09-17 12:52:37.683285
original_link:
  https://www.cqvip.com/doc/journal/3506649236?sign=70e5045a2e76048b286916fab6d38b05e3c19e7360b15af153fec0e8ac453fc0&expireTime=1789620663828&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: 2025-09-17 12:52:37.683739
original_link:
  https://www.cqvip.com/doc/journal/3508570122?sign=759e350bc104fe0d09b8801a8260acb39107f901208ab04a29d8f6ff27376633&expireTime=1789620663828&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
keywords: 高校思政课教学;基本路径;实践阻点;知识图谱
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: 2025-09-17 12:52:37.684221
original_link:
  https://www.cqvip.com/doc/journal/3508618697?sign=61729fd32077bab81b829f315abe3e6f55d35304d050d845097f4e51dc80df63&expireTime=1789620663828&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: 2025-09-17 12:52:37.684682
original_link:
  https://www.cqvip.com/doc/journal/3508542786?sign=d44d6a0fd74d39f366cc29e3a7e310adfd3ccb75c5be89cab3ae4a13743a9c7d&expireTime=1789620663828&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: 2025-09-17 12:52:37.685127
original_link:
  https://www.cqvip.com/doc/journal/3507499076?sign=95deeafefd2f2fb8e6a4eaebfd8d32dc273ac9e79c801f31bb5a74900afd4371&expireTime=1789620663828&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: 2025-09-17 12:52:37.685613
original_link:
  https://www.cqvip.com/doc/journal/3507488505?sign=760dc1037d097ed7674ca4b99072b851a3122287a72cc6459d835f4557397aa0&expireTime=1789620663828&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
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: 2025-09-17 12:52:37.686077
original_link:
  https://www.cqvip.com/doc/journal/3508556025?sign=429f1e488b104f4107fa6f0fc1384305983b657f2d2848bfcf26e2181cf1b0d2&expireTime=1789620663828&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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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: 2025-09-17 14:08:17.318783
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPlfo0_LvYhK5jcxYoyGcSfkAace5SsrL2XsbHYPDF3GRXWB2WN90rdhov4nK7ckkTNiKK1YxxxCaY8lSKWRM2BbVWXzflSn9gmU6P5cupw6uc0lsaGZFlq0mO15I2fNwBCIrW-WFpkcTrtiZBBSgrk2xKYW20ppU1JhJKzVC6D4w==&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: 2025-09-17 14:08:17.319522
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNYnfuutazzYhGmH3Y4mYCh5U6vXRlWv46HF_uT44DmX6FV0yD5l5CFGXZrccOgQBb6_wJyYYRkWolIM2QGRXCYBsCdc8LmdUOxsBl1fBZ63l1xGhyKfBQr_UafPv-z7c8BgAqyhaeodwuUGXWmgRFU0XOaG7zSk1m0HSDpI1Dwow==&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: 2025-09-17 14:08:17.320062
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPm1T4uHLEXj8-feFnqqXGKv4tXXCpgh8qEoTGQMwf_TC0x8RCibZApbhph8OOd0OwhdsHKuKN0cmEuT7byzwKoC4fYvHEAJg_b6ShQ0hXPt9CefRJwV4ctiZxmX43rJYfybmActrU6PupKGiG9s9JDRh7BO2ST0yafKfLNqNOLmQ==&uniplatform=NZKPT&language=CHS

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

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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: 2025-09-17 14:08:17.320590
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlN20eDv5X24x_GQhYLRIsiDgCZe660BF8SHfDqUttSFlcB9uFKpcWyZUMkohGm1UqpRYVpZEb2kjKQum4ET9odnlxp4pC8vL3g1rtSGVEKotsHxPiYZMNSb5AdVGpYcuTzg1a_BrWpDQ3Sb2gWNwpjk1oo1Ygi2WDk7XrvzUbbf3oj4RIzWbtZb&uniplatform=NZKPT&language=CHS

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

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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: 2025-09-17 14:08:17.321223
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlN20eDv5X24x_GQhYLRIsiDgCZe660BF8SHfDqUttSFlccNG0ipV7TGx4W57t2wno6G_FUXU7UcbIVE5VwKmWp5agPuZKybPzPvE61TQjbfATQ0cXo577jnH7Ec-Oe8dVDpdyPaguCTjc04dzYh3LVxeQOP5riGA0zaSgK7ybuCyw==&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\u4ee5\u53ca\u4f20\u64ad\u6e20\u9053\u788e\u7247\u5316\uff0c\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u6280\u672f\u5728\u4fe1\u606f\u7ba1\u7406\u3001\u4e2a\u6027\u5316\u63a8\u8350\u548c\u8206\u60c5\u76d1\u6d4b\u7b49\u9886\u57df\u53d8\u5f97\u8d8a\u53d1\u91cd\u8981\u3002\u7136\u800c\uff0c\u73b0\u6709\u65b9\u6cd5\u5728\u5904\u7406\u5355\u6807\u7b7e\u4e0e\u5c42\u6b21\u6807\u7b7e\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u65f6\uff0c\u9762\u4e34\u4e0a\u4e0b\u6587\u4fe1\u606f\u63d0\u53d6\u3001\u8bed\u4e49\u8868\u8fbe\u548c\u5c42\u6b21\u6807\u7b7e\u5229\u7528\u7b49\u591a\u65b9\u9762\u6311\u6218\u3002", "contribution": "\u63d0\u51fa\u4e86\u4e24\u5957\u591a\u5143\u7279\u5f81\u878d\u5408\u7684\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u65b0\u6a21\u578b\uff0c\u89e3\u51b3\u4e86\u884c\u4e1a\u5185\u957f\u671f\u5b58\u5728\u7684\u8bed\u4e49\u548c\u7ed3\u6784\u7279\u5f81\u6574\u5408\u96be\u9898\uff0c\u4e3a\u5b9e\u9645\u65b0\u95fb\u4fe1\u606f\u5904\u7406\u63d0\u4f9b\u4e86\u66f4\u52a0\u51c6\u786e\u548c\u9ad8\u6548\u7684\u6280\u672f\u65b9\u6848\u3002", "method": "\u9488\u5bf9\u5355\u6807\u7b7e\u5206\u7c7b\uff0c\u8bbe\u8ba1\u4e86CBDN\u6a21\u578b\uff0c\u878d\u5408\u5168\u8bcd\u63a9\u7801BERT-wwm\u3001\u53cc\u5411\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc\u3001\u591a\u5934\u6ce8\u610f\u529b\u548c\u6539\u8fdb\u91d1\u5b57\u5854\u5377\u79ef\u7f51\u7edc\u63d0\u53d6\u5e76\u4ea4\u4e92\u591a\u5143\u7279\u5f81\uff1b\u9488\u5bf9\u5c42\u6b21\u5206\u7c7b\uff0c\u6784\u5efa\u4e86\u4ee5RoBERTa\u4e3a\u57fa\u7840\u3001\u5f15\u5165\u63d0\u793a\u5d4c\u5165\u548c\u52a8\u6001\u5c42\u6b21\u7279\u5f81\u878d\u5408\u7684DHFPE\u6a21\u578b\uff0c\u5e76\u7ed3\u5408\u6ce8\u610f\u529b\u673a\u5236\u5bf9\u6bd4\u5b66\u4e60\u63d0\u5347\u5206\u7c7b\u533a\u5206\u5ea6\u3002", "objective": "\u7814\u7a76\u5e76\u63d0\u51fa\u80fd\u591f\u514b\u670d\u8bed\u4e49\u7279\u5f81\u6a21\u7cca\u3001\u4e0a\u4e0b\u6587\u4fe1\u606f\u63d0\u53d6\u96be\u5ea6\u5927\u4ee5\u53ca\u5c42\u6b21\u6807\u7b7e\u5229\u7528\u4e0d\u8db3\u7b49\u95ee\u9898\u7684\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u65b0\u65b9\u6cd5\u3002", "results": "\u7cfb\u7edf\u5b9e\u9a8c\u8868\u660e\uff0cCBDN\u4e0eDHFPE\u5206\u522b\u5728\u5355\u6807\u7b7e\u548c\u5c42\u6b21\u6807\u7b7e\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u5747\u53d6\u5f97\u4e86\u4f18\u5f02\u6027\u80fd\uff0c\u6709\u6548\u63d0\u5347\u4e86\u8bed\u4e49\u8868\u8fbe\u8d28\u91cf\u53ca\u5c42\u6b21\u6807\u7b7e\u7684\u5229\u7528\u6548\u7387\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: 2025-09-17 14:08:17.321816
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlP-2PunmHhfw2nzhXorutG7xPRbxeUG6g3nNJ2-EbmdXkbUHdjFX8oom1farnX-LldKllZun2Jir-PBLlVXJI1YEmlHM5VzMZ4-1v9RifVvucyh6hmYNPDBi_Wx3b9oEvRjqXMV6x4pdWHP4frIK7vDAZj7S6136sTKAHag3-rVUg==&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\u5927\u91cf\u5224\u4f8b\u548c\u6cd5\u5f8b\u6587\u6863\uff0c\u4f46\u5982\u4f55\u6709\u6548\u7ba1\u7406\u548c\u68c0\u7d22\u8fd9\u4e9b\u6587\u672c\u6210\u4e3a\u6311\u6218\u3002\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u4f5c\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7684\u91cd\u8981\u65b9\u5411\uff0c\u53ef\u4ee5\u63d0\u5347\u6cd5\u5f8b\u6587\u6863\u5904\u7406\u6548\u7387\uff0c\u4f46\u73b0\u6709\u65b9\u6cd5\u5728\u7279\u5f81\u63d0\u53d6\u3001\u6807\u7b7e\u5173\u7cfb\u5efa\u6a21\u7b49\u65b9\u9762\u4f9d\u7136\u5b58\u5728\u4e0d\u8db3\u3002", "contribution": "\u63d0\u51fa\u4e86\u65b0\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u9488\u5bf9\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u7684\u5b9e\u9645\u9700\u6c42\uff0c\u5728\u7279\u5f81\u878d\u5408\u4e0e\u6807\u7b7e\u8bed\u4e49\u5efa\u6a21\u65b9\u9762\u505a\u4e86\u521b\u65b0\uff0c\u6709\u6548\u63d0\u5347\u4e86\u53f8\u6cd5\u6587\u672c\u81ea\u52a8\u5206\u7c7b\u80fd\u529b\uff0c\u4e3a\u667a\u80fd\u53f8\u6cd5\u7cfb\u7edf\u5efa\u8bbe\u63d0\u4f9b\u6280\u672f\u652f\u6301\u3002", "method": "\u63d0\u51fa\u4e86\u4e24\u79cd\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u6a21\u578b\uff0c\u91c7\u7528\u591a\u7279\u5f81\u878d\u5408\u548c\u6807\u7b7e\u5d4c\u5165\u6280\u672f\uff0c\u9488\u5bf9\u5c42\u6b21\u7279\u5f81\u5efa\u6a21\u3001\u6807\u7b7e\u4e0e\u6587\u672c\u5173\u8054\u3001\u5168\u5c40\u4fe1\u606f\u538b\u7f29\u635f\u5931\u548c\u6807\u7b7e\u76f8\u5173\u6027\u5145\u5206\u5efa\u6a21\u7b49\u95ee\u9898\u8fdb\u884c\u6539\u8fdb\u3002", "objective": "\u63d0\u5347\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\uff0c\u89e3\u51b3\u73b0\u6709\u6a21\u578b\u5728\u7279\u5f81\u63d0\u53d6\u3001\u6807\u7b7e\u5173\u8054\u5efa\u6a21\u53ca\u5168\u5c40\u4fe1\u606f\u5229\u7528\u7b49\u65b9\u9762\u7684\u95ee\u9898\u3002", "results": "\u6539\u8fdb\u540e\u7684\u6a21\u578b\u5728\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u66f4\u9ad8\u7684\u51c6\u786e\u7387\u548c\u66f4\u597d\u7684\u6807\u7b7e\u76f8\u5173\u6027\u5efa\u6a21\u8868\u73b0\uff0c\u663e\u8457\u63d0\u5347\u4e86\u6587\u6863\u68c0\u7d22\u6548\u7387\uff0c\u589e\u5f3a\u4e86\u5206\u7c7b\u4e00\u81f4\u6027\u3002"}


author: 曹文宇
date: 2025-06-05
keywords: 海量文本分类;;图卷积神经网络;;BERT;;多层次注意力机制;;特征融合;
source: 内蒙古科技大学
site: 知网
title: 基于深度学习的海量文本分类技术研究
created_at: 2025-09-17 14:08:17.322334
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMka6vOWq0xJuyhG0GzMAi4UjfBAF20FI7VnlzfGmzWVyWG3ji--iZpezstrAhmlEeQSy9oXeqK_f9dgIxg3xdWKlBfHgduBgzfixs91Biz3YXMuaLKShwhDZVaUJ7i2C7aQmxuEtBby-TAQBAlU95X9APQTJ8a-ofMh9wTJFNQ2w==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u6d77\u91cf\u6587\u672c\u5206\u7c7b\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u4e2d\u7684\u6838\u5fc3\u4efb\u52a1\uff0c\u968f\u7740\u4e92\u8054\u7f51\u548c\u793e\u4ea4\u5a92\u4f53\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u9762\u5bf9\u9ad8\u7ef4\u3001\u975e\u7ed3\u6784\u5316\u4e0e\u52a8\u6001\u53d8\u5316\u7684\u6d77\u91cf\u6587\u672c\u6570\u636e\uff0c\u4f20\u7edf\u57fa\u4e8e\u89c4\u5219\u548c\u673a\u5668\u5b66\u4e60\u7684\u65b9\u6cd5\u5df2\u65e0\u6cd5\u6ee1\u8db3\u9ad8\u6548\u548c\u51c6\u786e\u5206\u7c7b\u7684\u9700\u6c42\u3002", "contribution": "\u8bba\u6587\u521b\u65b0\u6027\u5730\u7ed3\u5408\u53d8\u5206\u7ed3\u6784\u548c\u52a8\u6001\u6743\u91cd\u673a\u5236\u63d0\u5347\u56fe\u795e\u7ecf\u7f51\u7edc\u6027\u80fd\uff0c\u5e76\u9488\u5bf9BERT\u4f18\u5316\u5168\u5c40\u4fe1\u606f\u5efa\u6a21\uff0c\u4e30\u5bcc\u4e86\u6df1\u5ea6\u5b66\u4e60\u5728\u6587\u672c\u5206\u7c7b\u9886\u57df\u7684\u7406\u8bba\u4e0e\u5e94\u7528\u65b9\u6cd5\u3002", "method": "\u8bba\u6587\u5206\u522b\u9488\u5bf9\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5f15\u5165\u53d8\u5206\u7ed3\u6784\u548c\u52a8\u6001\u8fb9\u6743\u91cd\u5b66\u4e60\u673a\u5236\uff0c\u63d0\u51fa\u4e00\u79cd\u6539\u8fdb\u7684VSD-GCN\u6a21\u578b\uff0c\u540c\u65f6\u5206\u6790BERT\u9884\u8bad\u7ec3\u6a21\u578b\u5728\u6355\u6349\u5168\u5c40\u62d3\u6251\u4fe1\u606f\u7684\u4e0d\u8db3\uff0c\u5e76\u63d0\u51fa\u65b0\u7684\u4f18\u5316\u7b56\u7565\u4ee5\u589e\u5f3a\u5176\u4e0a\u4e0b\u6587\u5efa\u6a21\u80fd\u529b\u3002", "objective": "\u672c\u7814\u7a76\u65e8\u5728\u9488\u5bf9\u73b0\u6709\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\uff08\u5982\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548cBERT\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff09\u5728\u6587\u672c\u5206\u7c7b\u4e2d\u5b58\u5728\u7684\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u4e0e\u5168\u5c40\u8bed\u4e49\u5efa\u6a21\u80fd\u529b\u4e0d\u8db3\u7684\u95ee\u9898\uff0c\u63d0\u51fa\u65b0\u7684\u6539\u8fdb\u7b56\u7565\u4ee5\u63d0\u5347\u5206\u7c7b\u51c6\u786e\u5ea6\u548c\u6cdb\u5316\u80fd\u529b\u3002", "results": "\u6240\u63d0\u51fa\u7684\u6539\u8fdb\u6a21\u578b\u6709\u6548\u63d0\u5347\u4e86\u5927\u89c4\u6a21\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u6cdb\u5316\u80fd\u529b\uff0c\u80fd\u591f\u66f4\u597d\u5730\u9002\u5e94\u4e0d\u540c\u6587\u672c\u6837\u672c\u7684\u591a\u6837\u6027\uff0c\u4f18\u5316\u4e86\u5168\u5c40\u8bed\u4e49\u548c\u4e0a\u4e0b\u6587\u7279\u5f81\u7684\u878d\u5408\u8868\u8fbe\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: 2025-09-17 14:08:17.322853
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNgAsCpfoHZlEJ2iTwrHC6FYTVHzwT8lB9uwTD1eJvscBLSJG1CqJsF6t-T2jT-5xmvClNI6ClkfdUWXA0Rk7B9Ul6evm0fAxz7-3Hl3hU812VUQjGuwQAVymsNF5bUsR4m5_SVAYYWuGMYzHMWEExhwIKvo5VKHtbn0_X6bSoEaQ==&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\u5f52\u7c7b\u5927\u91cf\u533b\u7597\u6587\u672c\uff0c\u5e2e\u52a9\u533b\u751f\u9ad8\u6548\u68c0\u7d22\u4fe1\u606f\uff0c\u63d0\u9ad8\u8bca\u65ad\u6548\u7387\u548c\u51c6\u786e\u7387\u3002\u7531\u4e8e\u533b\u7597\u6587\u672c\u4e13\u4e1a\u6027\u5f3a\u3001\u672f\u8bed\u5bc6\u96c6\uff0c\u5bf9\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u4e25\u8c28\u6027\u6709\u6781\u9ad8\u8981\u6c42\uff0c\u73b0\u6709\u65b9\u6cd5\u5728\u5229\u7528\u7ed3\u6784\u5316\u4fe1\u606f\u548c\u9ad8\u9636\u90bb\u5c45\u5173\u7cfb\u65b9\u9762\u4ecd\u6709\u4e0d\u8db3\u3002", "contribution": "\u9996\u6b21\u5c06\u5f02\u6784\u56fe\u6269\u6563\u7f51\u7edc\u5e94\u7528\u4e8e\u533b\u7597\u6587\u672c\u5206\u7c7b\uff0c\u521b\u65b0\u6027\u5730\u5efa\u6a21\u7ed3\u6784\u5173\u7cfb\u548c\u8fdc\u7a0b\u4f9d\u8d56\uff0c\u663e\u8457\u63d0\u9ad8\u4e86\u5206\u7c7b\u51c6\u786e\u6027\uff0c\u4e3a\u667a\u6167\u533b\u7597\u6587\u672c\u5904\u7406\u63d0\u4f9b\u4e86\u65b0\u601d\u8def\u3002", "method": "\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u5f02\u6784\u56fe\u6269\u6563\u7f51\u7edc\u7684\u533b\u7597\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff08MGD\uff09\uff0c\u901a\u8fc7\u6784\u5efa\u5305\u542b\u5355\u8bcd-\u5355\u8bcd\u3001\u53e5\u5b50-\u53e5\u5b50\u53ca\u5355\u8bcd-\u53e5\u5b50\u5173\u7cfb\u7684\u6587\u672c\u5f02\u6784\u56fe\uff0c\u5e76\u7ed3\u5408\u56fe\u6269\u6563\u65b9\u6cd5\u3001\u5f02\u6784\u56fe\u5377\u79ef\u7f51\u7edc\u548c\u591a\u5c42\u611f\u77e5\u5668\u5b9e\u73b0\u5206\u7c7b\u3002", "objective": "\u9488\u5bf9\u73b0\u6709\u533b\u7597\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u5ffd\u7565\u6587\u672c\u7ed3\u6784\u4fe1\u606f\u548c\u96be\u4ee5\u6355\u6349\u9ad8\u9636\u90bb\u5c45\u5173\u7cfb\u7684\u95ee\u9898\uff0c\u63d0\u51fa\u65b0\u7684\u5206\u7c7b\u65b9\u6cd5\u4ee5\u63d0\u9ad8\u5206\u7c7b\u6548\u679c\u3002", "results": "\u901a\u8fc7\u6784\u5efa\u6587\u672c\u5f02\u6784\u56fe\u5e76\u5f15\u5165\u53e5\u5b50-\u53e5\u5b50\u7b49\u591a\u7ef4\u5173\u7cfb\uff0c\u6709\u6548\u63d0\u5347\u4e86\u5206\u7c7b\u6027\u80fd\u3002\u6d88\u878d\u5b9e\u9a8c\u663e\u793a\u8be5\u65b9\u6cd5\u80fd\u591f\u6355\u6349\u6587\u672c\u5168\u5c40\u4fe1\u606f\u548c\u957f\u7a0b\u4f9d\u8d56\uff0c\u663e\u8457\u4f18\u4e8e\u4f20\u7edf\u65b9\u6cd5\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: 2025-09-17 14:08:17.323337
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNCBnTBIkF-H6EozgF_XjM9Ax7ZdYHogt9JMroD-9RgNxpkydeVIjPHo43boQx1neV1zv03Wj4UVd5MjUW8P3MznPtkwEII0XERKSSq1G3yCfXGwksP9tK2-8z77uzV9HzjmxkrB0y-7zPTwwkOTUiLcvJpaLsFTzeD8dh8qWboDw==&uniplatform=NZKPT&language=CHS

summary:
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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: 2025-09-17 14:08:17.323828
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNCBnTBIkF-H6EozgF_XjM9Ax7ZdYHogt9JMroD-9RgN2mMv1LM8Bdo7XqV7D6yRmdWrEchY7kFuYNRjTS55hYqi2tW5055NwFZAZPXAQWMctz6x6PilT3OWm6zJSYsjvonefgrfGzFXW9_zcp_Yx8oH2xEivl37yHfy7L_M0YYxzqO9i1H_J3H&uniplatform=NZKPT&language=CHS

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

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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: 2025-09-17 14:08:17.324337
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlO_zavgSeIhxci_fMP8gHm69pX06kSmHm3LHdH26ugyTHWa4CM3zZmxd1QLTevlBBvHjEcbFMuUmTMauSPGz8XP3t7A2zILwdvwxADYFCtqjfxxa_XxYinaaA1ycOoT_AspUnte8FXcIHV8VMfVL4A0s4dULEOH134IODBfngBItA==&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: 孙鑫
date: 2025-05-27
keywords: 自然语言处理;;少样本文本分类;;元学习;;对比学习;
source: 黑龙江大学
site: 知网
title: 基于元学习的少样本文本分类方法研究
created_at: 2025-09-17 14:08:17.324869
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPtHalna7m4QK3UGFYOnFmVVMvzv_ZzaAz5hZAQq-IoIEm-nfZTLMUW9CkwnQlRjvtD7S9WOF3n-ZG_pYiH1ZJsW703Pipz7OJv6m48NEbCRm-4FU6cudl71gfi80F-CbePKpvs0dImzmEA-9TYibggIb4LB2_hcDeImOAu1QLW9g==&uniplatform=NZKPT&language=CHS

summary:
  文本分类任务是自然语言处理(NLP)领域的基本问题之一,有着十分广泛的应用场景,如情感分类、虚假新闻检测、实体识别等。在文本分类过程中,模型将输入的样本分配至一个或多个类别中。传统的文本分类方法通常为有监督算法,需要大量的有标签数据对模型进行训练,这在现实的应用场景中往往难以满足。针对这个问题,少样本文本分类任务应运而生。少样本文本分类是指在模型训练过程中,每个类别仅有少数训练样本,通过一定的逻辑将文本分配到一个或多个类别中,有着十分广泛的应用前景。 近年来,元学习方法已经成为解决少样本学习任务的主要方式。虽然现有一些基于元学习的少样本文本分类方法已经取得了良好表现,但仍存在一些待改进的缺点。现有基于元学习的少样本文本分类方法通常聚合样本特征来获取类别原型的嵌入表示,类别原型将随着选取样本的不同而发生变化,具有不稳定性。其次,在少样本分类场景下,模型难以学习到足够的语义信息,进一步导致了模型的性能下降,同时也存在易过拟合的问题。 本文旨在提高少样本场景下文本分类任务性能,提出如下三个方法。 第一,本文提出了基于元学习与外部知识的少样本文本分类算法。本方法使用外部知识提高类别原型的稳定性,...

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author: 周明军
date: 2025-05-24
keywords: 藏文;;文本分类;;预训练语言模型;;提示学习;
source: 西藏大学
site: 知网
title: 基于提示学习的藏文文本分类研究
created_at: 2025-09-17 14:08:17.325367
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPPqqCPz-sp01v6KCItOrlnl6AZsQ1z2USPxWUO4giNY1RbvmZURmepbh1wu5rsXyrI6knu4xVAVompnpHDB0oVlocLaYBcd6vGPfTm8gPxLwanXZLiVRZpyQupOaySRPx-m2yFCXGi0cVCnG1Ub_C_9T1-Ci9YGP9rKpQ8qZ7TVg==&uniplatform=NZKPT&language=CHS

summary:
  藏文文本分类作为藏语自然语言处理(Natural Language Processing,NLP)中的关键任务,广泛应用于舆情监测、新闻推送、电子邮件筛选等领域。然而,当前该任务面临两个核心挑战:一是藏文标注数据资源极为有限,二是现有藏文预训练语言模型(Pre-trained Language Model,PLM)在小样本条件下的性能表现尚难令人满意。因此,如何在低资源场景下有效提升藏文文本分类性能成为亟需解决的重要问题。近年来,提示学习(Prompt Learning)作为一种高效的模型微调范式,在少样本学习中表现出显著优势。本文围绕提示学习在藏文文本分类中的应用展开研究,选取跨中国少数民族语言预训练模型CINO(A Chinese Minority Pre-trained Language Model)作为基础模型,设计并验证了多种提示学习微调策略,旨在为藏语自然语言处理任务提供更具实用性的技术路径。主要工作与创新如下: (1)提出符合藏文语言规范的文本预处理方法。针对藏文文本在标点符号使用上的特殊性,本文设计了一种更符合藏文词句构成规律的预处理方法,并构建了一个六分类新闻原始文本数...

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author: 王京
date: 2025-05-01
keywords: 少样本学习;;元学习;;主动学习;;生成对抗网络;;自适应合成抽样方法;
source: 山东财经大学
site: 知网
title: 基于改进元学习算法的少样本文本分类研究
created_at: 2025-09-17 14:08:17.325836
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNp_gbjeEFafF1C1qE_x1ZRYon4BWevs-Z4987UyxhSa45yWB-rgMVlKpqCVxl5Lz1kHO6H6IP2UnI8eAFGOvBqdNp3Kzm5niZat6JQVuk1kSjI3ao7TNi7Opl5PHs2TvO7a8EN-gxyCb72wusnYs1PdPGWaJz44ViJYb8TdBRIdQ==&uniplatform=NZKPT&language=CHS

summary:
  在当今信息时代,文本数据的生成速度远远超过了人类处理和理解的能力。所以,如何有效地从未结构化的文本数据中提取出有价值的信息,成为一个亟需解决的问题。目前,传统基于机器学习的文本分类方法依赖于人工制定的文本特征提取规则,在表达文本特征方面较弱,难以捕捉文本间的复杂关系。基于深度学习的方法无需人工提取特征,而是通过深度学习模型对文本进行编码,提高分类准确性。但是,深度学习方法却高度依赖大量标注数据。如何在只有少量标注样本的情况下实现高效准确的文本分类,已然成为了一个重要的研究课题。 因此,本文基于MLADA框架(1)提出图神经驱动的元主动学习模型,解决传统方法过度依赖标注数据以及领域差异导致负迁移使模型性能不佳的问题;(2)提出ADASYN增强的元学习模型,解决模型泛化能力不足以及类别不均衡下性能偏差的问题。具体研究内容与贡献如下: 1.图神经驱动的元主动学习模型GMAL:在主动学习阶段,使用Text GCN采样策略,它整合词袋模型、TF-IDF向量等多类型特征来构建文本图,其中文档-单词边与单词-单词边分别依据TF-IDF和PMI计算权重,以此捕捉语义关系。之后通过图卷积操作更新节点特征,...

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author: 武同心;纪鑫;杨成月;陈屹婷;杨智伟
date: 2025-04-27 09:06
keywords: 深度学习;;电力领域;;文本分类;;卷积神经网络;;注意力机制;
source: 中国电力
site: 知网
title: 基于双层注意力机制的电力文本分类模型
created_at: 2025-09-17 14:08:17.326338
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMiJO3sa-5mUKC8IX02AY7ygiHtzzc0-OIPpLggAigqBxKwW0e-LFZ3S9z1c2lbySsyUB3_UKzoCC0POitYqLEoQtLDG-Bv0kMQ-eCnxaq7IjJJ769sBqamRmN8PqWG544mhNqgb2PSdWtFYQkAhuoHhbYequhrbhSfrwu0NIb9Cg==&uniplatform=NZKPT&language=CHS

summary:
  电力领域中存在大量的中文文本数据，传统文本挖掘方法存在分词难度大、文本特征提取具有局限性、处理文本中的复杂关系时效果不好等问题，严重限制了对电力信息的深度理解与分类。基于此，提出了一种结合文本卷积网络(text convolutional neural networks， TextCNN)与注意力(Attention)机制的电力文本分类模型，对输入层、TextCNN层、第一个注意力层、池化层、第二个注意力层以及输出层进行了分层优化设计，并通过实验对模型进行了验证。结果显示，TextCNN-Attention模型在电力文本数据集上的文本分类准确率达到了96.8%，精确率达到了86.3%，召回率达到了90.3%，综合评价值达到了88.2%，表明TextCNN-Attention模型可有效提升电力文本分类的效率，为深度学习算法在电力文本分类领域的应用提供了经验借鉴。

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author: 许惠惠
date: 2025-04-25
keywords: Mega模型;;文本分类;;长文本生成;;门控注意力机制;;指数移动平均;
source: 信息技术与信息化
site: 知网
title: 基于Mega模型的文本分类与长文本生成研究
created_at: 2025-09-17 14:08:17.326812
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNlGaoDgXkrd5IjR55xyiSyCArqviXGuocglDFcYmRQJzYmgJsagD0t1iJffTP08vLK5amJLyYtCzz8gj77D52EymnHfYyM0-1Kpf_oWZxuBfgKI-B85Tk8Idar3114cuRHmI7MpGL6v-V7pZ9T68GsjUbRyVT1ThGpcTCkO0ddHg==&uniplatform=NZKPT&language=CHS

summary:
  随着社交媒体、新闻平台和电商评论等领域中长文本数据的激增，传统模型如 RNN 和 LSTM 在建模长距离依赖方面存在局限，而 Transformer 在处理超长文本时计算开销巨大。为此，文章提出基于 Mega(moving average equipped gated attention)模型的长文本分类与生成方法。通过引入指数移动平均(exponential moving average,EMA)和门控注意力机制，Mega模型增强了长距离依赖建模能力，并通过Mega-chunk 机制提升计算效率。在文本分类任务中，Mega在 AG News、IMDB 和 Amazon Reviews 数据集上的表现优于RNN、LSTM、Tansformer 和Longformer，尤其在长文本分类中展现了更高的准确率和计算效率。综上，Mega 模型通过创新设计，在长文本处理任务中实现了性能与效率的平衡，适用于智能问答、新闻摘要等实际场景。

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author: 赵青
date: 2025-03-25
keywords: 农业乡土文化;;文本分类;;卷积神经网络;;双向长短期记忆;;神经网络;
source: 信息技术
site: 知网
title: 基于CNN-Bi-LSTM的农业乡土文化文本分类模型构建
created_at: 2025-09-17 14:08:17.327281
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlO_Xo9mnGh3M1JKHrHzuXhlDHk5FsAL04st3RYts5TlgKw9BpBNz4i8yVIJWBr48FYKE2zsvxhc1FhyvqqdN25shLzbfcxNDCN6ZuC32Mn4VOPh6ojva12ht2J3SC9CaYkDkwwbe3zh-jvKfakg8WBTpsCtMmC8yrAcFTGcPiu2_w==&uniplatform=NZKPT&language=CHS

summary:
  针对当前网络媒体中的农业文本分类提取困难的问题，文中提出一种基于多层卷积神经网络结构(CNN)结合双向长短期记忆网络(Bi-LSTM)的文本分类模型。模型采用Jieba分词工具进行文本预处理，在CNN模型的文本特征提取的基础上，引用LSTM门控机制优化结构。实验表明，运用该模型对农业乡土文化文本进行分类时，数据获取精度均保持在90%左右。实验证明了基于CNN-Bi-LSTM的优化模型在农业乡土文化文本分类识别中具备较高的精度性能和稳定性。

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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: 2025-09-17 14:08:17.327770
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAyGgh4aHdqazN4dA%3D%3D

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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: 2025-09-17 14:08:17.328238
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA0Ggh4aHdqazN4dA%3D%3D

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

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  {"background": "\u5728\u8de8\u8bed\u8a00\u6587\u672c\u5206\u7c7b\u8fc7\u7a0b\u4e2d\uff0c\u8bed\u8a00\u5dee\u5f02\u548c\u6587\u672c\u7279\u5f81\u9009\u62e9\u5e38\u5e38\u5bfc\u81f4\u8bed\u4e49\u4fe1\u606f\u7684\u504f\u79bb\u4e0e\u4e22\u5931\uff0c\u8fd9\u5f71\u54cd\u4e86\u6a21\u578b\u7684\u5206\u7c7b\u6548\u679c\u3002", "contribution": "\u63d0\u51fa\u4e86\u7ed3\u5408\u53e5\u5411\u91cf\u52a0\u6743\u548c\u76f8\u4f3c\u5ea6\u7b97\u6cd5\u7684\u8de8\u8bed\u8a00\u6587\u672c\u5206\u7c7b\u65b0\u6846\u67b6\uff0c\u6709\u6548\u51cf\u5c11\u4e86\u8bed\u4e49\u504f\u79bb\u548c\u4e22\u5931\uff0c\u5728\u5206\u7c7b\u6027\u80fd\u4e0a\u4f18\u4e8e\u73b0\u6709\u65b9\u6cd5\uff0c\u4e3a\u591a\u8bed\u8a00\u6587\u672c\u5904\u7406\u9886\u57df\u5e26\u6765\u4e86\u521b\u65b0\u548c\u63d0\u5347\u3002", "method": "\u91c7\u7528SBERT\u9884\u8bad\u7ec3\u6a21\u578b\u83b7\u5f97\u53e5\u5b50\u8868\u793a\uff0c\u901a\u8fc7\u53e5\u5411\u91cf\u65cb\u8f6c\u76f8\u4f3c\u5ea6\u65b9\u6cd5\u5bf9\u6587\u672c\u5185\u53e5\u5b50\u8fdb\u884c\u52a0\u6743\u5e76\u751f\u6210\u6587\u672c\u5411\u91cf\uff0c\u968f\u540e\u7ed3\u5408\u673a\u5668\u5b66\u4e60\u548c\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u65b9\u6cd5\u4ee5\u51cf\u5c11\u56e0\u8bed\u8a00\u5dee\u5f02\u548c\u7279\u5f81\u9009\u62e9\u5e26\u6765\u7684\u8bed\u4e49\u504f\u79bb\u548c\u4e22\u5931\uff0c\u63d0\u5347\u8de8\u8bed\u8a00\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u7387\u3002", "results": "\u5728\u591a\u8bed\u8a00\u6570\u636e\u96c6\uff08\u5305\u62ec\u4e2d\u3001\u82f1\u3001\u4fc4\u3001\u6cd5\u3001\u897f\u73ed\u7259\u8bed\u548cReuters\uff09\u4e0a\u65b9\u6cd5\u51c6\u786e\u7387\u7ea6\u4e3a96%\uff0c\u5e76\u5728\u53ec\u56de\u7387\u3001\u7cbe\u786e\u7387\u548cF1\u503c\u7b49\u6307\u6807\u4e0a\u8f83\u73b0\u6709\u65b9\u6cd5\u6709\u660e\u663e\u63d0\u5347\u3002"}


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: 2025-09-17 14:08:17.328731
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA1Ggh4aHdqazN4dA%3D%3D

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

parsed_summary:
  {"background": "\u968f\u7740\u8b66\u60c5\u6587\u672c\u6570\u636e\u91cf\u7684\u589e\u52a0\uff0c\u4f20\u7edf\u65b9\u6cd5\u5728\u667a\u80fd\u5316\u5904\u7406\u548c\u5206\u7c7b\u65b9\u9762\u9762\u4e34\u6311\u6218\uff0c\u8feb\u5207\u9700\u8981\u66f4\u9ad8\u6548\u7684\u8b66\u60c5\u6587\u672c\u5206\u7c7b\u6280\u672f\u4ee5\u8f85\u52a9\u516c\u5b89\u90e8\u95e8\u7684\u6570\u636e\u5206\u6790\u3002", "contribution": "\u8be5\u7814\u7a76\u63d0\u51fa\u4e86\u4e00\u79cd\u7ed3\u5408BERT\u4e0e\u4f18\u5316DPCNN\u7684\u521b\u65b0\u6587\u672c\u5206\u7c7b\u6a21\u578b\uff0c\u663e\u8457\u63d0\u5347\u8b66\u60c5\u6587\u672c\u5206\u7c7b\u7cbe\u5ea6\uff0c\u4e3a\u516c\u5b89\u90e8\u95e8\u8b66\u60c5\u5206\u6790\u548c\u7814\u5224\u63d0\u4f9b\u4e86\u53ef\u9760\u7684\u6570\u636e\u652f\u6301\u3002", "method": "\u6784\u5efa\u57fa\u4e8eBERT-DPCNN\u7684\u6587\u672c\u5206\u7c7b\u6a21\u578b\uff0c\u5229\u7528BERT\u9884\u8bad\u7ec3\u6a21\u578b\u751f\u6210\u6587\u672c\u8bcd\u5411\u91cf\uff0c\u5e76\u901a\u8fc7\u4f18\u5316DPCNN\u4e2d\u7684\u6fc0\u6d3b\u51fd\u6570\u548c\u52a8\u6001\u5b66\u4e60\u7387\u6765\u63d0\u5347\u5206\u7c7b\u6548\u679c\uff0c\u968f\u540e\u4e0e\u591a\u79cd\u4e3b\u6d41\u6a21\u578b\u8fdb\u884c\u5bf9\u6bd4\u5b9e\u9a8c\u3002", "objective": "\u63d0\u51fa\u5e76\u9a8c\u8bc1\u4e00\u79cd\u80fd\u591f\u63d0\u5347\u8b66\u60c5\u6587\u672c\u5206\u7c7b\u6027\u80fd\u7684\u65b0\u6a21\u578b\uff0c\u4ee5\u5b9e\u73b0\u6d77\u91cf\u8b66\u60c5\u6570\u636e\u7684\u9ad8\u6548\u81ea\u52a8\u5316\u5206\u7c7b\u3002", "results": "BERT-DPCNN\u6a21\u578b\u5728\u4e8c\u5206\u7c7b\u4efb\u52a1\u4e2d\u51c6\u786e\u7387\u8d85\u8fc798%\uff0c\u5341\u4e00\u5206\u7c7b\u4efb\u52a1\u4e2d\u51c6\u786e\u7387\u8d85\u8fc782%\uff0c\u5176\u51c6\u786e\u7387\u3001\u53ec\u56de\u7387\u548c\u7cbe\u51c6\u7387\u5747\u4f18\u4e8e\u5176\u4ed6\u5bf9\u6bd4\u6a21\u578b\u3002"}


author: 韩普;王志伟;杜文文
date: 2025年4期
keywords: 法律文本分类;深度图神经网络;细粒度分类;解耦合;注意力扩散
quote: 0
source: 《数据分析与知识发现》
site: 万方
title: 基于深度注意力扩散图神经网络的法律文本细粒度分类方法
created_at: 2025-09-17 14:08:17.329220
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA0MDEyGgh4aHdqazN4dA%3D%3D

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

parsed_summary:
  {"background": "\u6cd5\u5f8b\u6587\u672c\u7ec6\u7c92\u5ea6\u5206\u7c7b\u5bf9\u4e8e\u6cd5\u5f8b\u4fe1\u606f\u7ba1\u7406\u548c\u667a\u80fd\u5316\u5e94\u7528\u81f3\u5173\u91cd\u8981\uff0c\u4f46\u5728\u5904\u7406\u590d\u6742\u8bed\u4e49\u548c\u8fdc\u8ddd\u79bb\u4f9d\u8d56\u65f6\u5b58\u5728\u8fc7\u5ea6\u5e73\u6ed1\u7b49\u95ee\u9898\uff0c\u9650\u5236\u4e86\u5206\u7c7b\u6548\u679c\u7684\u63d0\u5347\u3002", "contribution": "\u8be5\u8bba\u6587\u9996\u6b21\u5c06\u6df1\u5ea6\u6ce8\u610f\u529b\u6269\u6563\u56fe\u795e\u7ecf\u7f51\u7edc\u7528\u4e8e\u6cd5\u5f8b\u6587\u672c\u7ec6\u7c92\u5ea6\u5206\u7c7b\uff0c\u6210\u529f\u7f13\u89e3\u4e86\u8fc7\u5ea6\u5e73\u6ed1\u95ee\u9898\u5e76\u589e\u5f3a\u4e86\u957f\u8ddd\u79bb\u8bed\u4e49\u5efa\u6a21\u80fd\u529b\uff0c\u4e3a\u6cd5\u5f8b\u667a\u80fd\u5316\u7ba1\u7406\u548cAI\u5e94\u7528\u5960\u5b9a\u4e86\u57fa\u7840\u3002", "method": "\u672c\u7814\u7a76\u63d0\u51fa\u57fa\u4e8e\u6df1\u5ea6\u6ce8\u610f\u529b\u6269\u6563\u56fe\u795e\u7ecf\u7f51\u7edc\u7684FLGNN\u6a21\u578b\uff0c\u5148\u7528BERT\u63d0\u53d6\u8bed\u4e49\u5d4c\u5165\uff0c\u518d\u6784\u5efa\u6587\u672c\u6709\u5411\u56fe\uff0c\u901a\u8fc7\u6df1\u5ea6\u56fe\u7f51\u7edc\u6316\u6398\u5168\u5c40\u56fe\u4fe1\u606f\uff0c\u5e76\u7ed3\u5408\u7279\u5f81\u878d\u5408\u4e0e\u8282\u70b9\u7ea7\u6ce8\u610f\u529b\u673a\u5236\u8fdb\u884c\u4f18\u5316\uff0c\u6700\u540e\u5b8c\u6210\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u6539\u8fdb\u7684\u6df1\u5ea6\u56fe\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u89e3\u51b3\u73b0\u6709\u65b9\u6cd5\u5728\u6cd5\u5f8b\u6587\u672c\u7ec6\u7c92\u5ea6\u5206\u7c7b\u4e2d\u7684\u957f\u8ddd\u79bb\u4ea4\u4e92\u80fd\u529b\u5f31\u548c\u7279\u5f81\u8868\u8fbe\u4e0d\u8db3\u7684\u95ee\u9898\u3002", "results": "FLGNN\u6a21\u578b\u5728PKULawData\u548cJSCLawData\u6570\u636e\u96c6\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u7684\u5206\u7c7b\u51c6\u786e\u7387\u63d0\u5347\uff0c\u5206\u522b\u6bd4BERT\u3001DADGNN\u4ee5\u53caRCNN\u57fa\u7ebf\u6a21\u578b\u67091.15\u81f34.19\u4e2a\u767e\u5206\u70b9\u7684\u63d0\u9ad8\u3002"}


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: 2025-09-17 14:08:17.329691
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2JqeWRkeDIwMjUwMjAwMhoIeGh3amszeHQ%3D

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

parsed_summary:
  {"background": "\u5728\u6570\u636e\u8981\u7d20\u65f6\u4ee3\uff0c\u5f00\u653e\u79d1\u5b66\u6570\u636e\u96c6\u5bf9\u51b3\u7b56\u7684\u5f71\u54cd\u529b\u6210\u4e3a\u8861\u91cf\u5176\u8d28\u91cf\u4e0e\u4ef7\u503c\u7684\u91cd\u8981\u6807\u51c6\uff0c\u81ea\u52a8\u5316\u63d0\u53d6\u548c\u8bc4\u4ef7\u79d1\u5b66\u6570\u636e\u96c6\u5bf9\u653f\u5e9c\u51b3\u7b56\u7684\u5f71\u54cd\u529b\u9700\u6c42\u65e5\u76ca\u7a81\u51fa\u3002", "contribution": "\u521b\u65b0\u6027\u5730\u878d\u5408\u8bcd\u6027\u7279\u5f81\uff0c\u7528\u95e8\u63a7\u673a\u5236\u63d0\u5347\u957f\u6587\u672c\u5206\u7c7b\u6027\u80fd\uff0c\u4e3a\u79d1\u5b66\u6570\u636e\u96c6\u51b3\u7b56\u5f71\u54cd\u529b\u8bc4\u4ef7\u63d0\u4f9b\u4e86\u6709\u6548\u7684\u65b0\u6280\u672f\u8def\u5f84\u3002", "method": "\u63d0\u51fa\u4e86\u57fa\u4e8e\u8bcd\u6027\u7279\u5f81\u878d\u5408\u7684\u957f\u6587\u672c\u5206\u7c7b\u65b9\u6cd5LTC-WF\uff0c\u901a\u8fc7\u5bf9\u6587\u672c\u4e2d\u4e0d\u540c\u8bcd\u6027\u4ea4\u53c9\u878d\u5408\u77ed\u8bed\u7ec4\u548c\u539f\u6587\u5d4c\u5165\uff0c\u8bbe\u8ba1\u62fc\u63a5\u878d\u5408\u548c\u95e8\u63a7\u878d\u5408\u5355\u5143\u5b9e\u73b0\u6587\u672c\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u80fd\u591f\u6709\u6548\u8bc4\u4ef7\u79d1\u5b66\u6570\u636e\u96c6\u51b3\u7b56\u5f71\u54cd\u529b\u7684\u957f\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u89e3\u51b3\u4ece\u653f\u5e9c\u516c\u6587\u4e2d\u8bc6\u522b\u4e0e\u79d1\u5b66\u6570\u636e\u96c6\u76f8\u5173\u5185\u5bb9\u7684\u95ee\u9898\u3002", "results": "\u5728\u6240\u6784\u5efa\u7684\u79d1\u5b66\u6570\u636e\u653f\u7b56\u6570\u636e\u96c6\u4e0a\uff0c\u8be5\u65b9\u6cd5\u8f83\u4e3b\u6d41\u957f\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u8868\u73b0\u66f4\u4f18\uff0c\u6709\u6548\u63d0\u5347\u4e86\u5bf9\u79d1\u5b66\u6570\u636e\u96c6\u51b3\u7b56\u5f71\u54cd\u529b\u7684\u5206\u7c7b\u4e0e\u8bc4\u4ef7\u7cbe\u5ea6\u3002"}


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: 2025-09-17 14:08:17.330188
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTUaCHhod2prM3h0

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

parsed_summary:
  {"background": "\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u9700\u8981\u4e3a\u4e00\u6bb5\u6587\u672c\u5206\u914d\u591a\u4e2a\u6807\u7b7e\uff0c\u73b0\u6709\u65b9\u6cd5\u5927\u91cf\u4f9d\u8d56\u4e8e\u6807\u7b7e\u5173\u7cfb\u7684\u9759\u6001\u5efa\u56fe\uff0c\u5bfc\u81f4\u5206\u7c7b\u7ed3\u679c\u53d7\u9650\u4e8e\u9884\u5148\u7edf\u8ba1\u7684\u6807\u7b7e\u5171\u73b0\u4fe1\u606f\uff0c\u96be\u4ee5\u6355\u6349\u5230\u5177\u4f53\u6587\u672c\u4e2d\u6807\u7b7e\u7684\u672c\u5730\u76f8\u5173\u6027\u3002", "contribution": "\u521b\u65b0\u6027\u5730\u5f15\u5165\u57fa\u4e8e\u5f53\u524d\u6587\u672c\u8bed\u5883\u7684\u52a8\u6001\u56fe\u6807\u7b7e\u5efa\u6a21\u548c\u7279\u5f81\u878d\u5408\u7b56\u7565\uff0c\u63d0\u5347\u4e86\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u7387\uff0c\u4e3a\u591a\u6807\u7b7e\u5206\u7c7b\u95ee\u9898\u63d0\u4f9b\u4e86\u65b0\u7684\u89e3\u51b3\u601d\u8def\u3002", "method": "\u8bbe\u8ba1\u52a8\u6001\u6807\u7b7e\u5173\u7cfb\u56fe\uff0c\u6839\u636e\u5f53\u524d\u6587\u672c\u6784\u5efa\u6807\u7b7e\u95f4\u76f8\u5173\u6027\uff0c\u5e76\u878d\u5408\u7279\u5f81\u8868\u793a\uff0c\u5f15\u5165\u56fe\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u6807\u7b7e\u7279\u5f81\u5efa\u6a21\uff0c\u6700\u540e\u7528\u4e8e\u591a\u6807\u7b7e\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u80fd\u591f\u7ed3\u5408\u5f53\u524d\u6587\u672c\u5185\u5bb9\u52a8\u6001\u5efa\u6a21\u6807\u7b7e\u95f4\u5173\u7cfb\u7684\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u7b97\u6cd5\uff0c\u63d0\u5347\u5206\u7c7b\u51c6\u786e\u6027\u3002", "results": "\u57283\u4e2a benchmark \u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u7ed3\u679c\u663e\u793a\uff0c\u6240\u63d0\u6a21\u578b\u5728\u591a\u6807\u7b7e\u5206\u7c7b\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u4f18\u5f02\u6027\u80fd\uff0c\u9a8c\u8bc1\u4e86\u65b9\u6cd5\u7684\u6709\u6548\u6027\u4e0e\u53ef\u884c\u6027\u3002"}


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: 2025-09-17 14:08:17.330667
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdocXp5eWhqMjAyNTAzMDAxGgh4aHdqazN4dA%3D%3D

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

parsed_summary:
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author: 谌文佳;杨琳;李金林
date: 2025年2期
keywords: 意图识别;医疗问答;语义相似度分类;深度学习
quote: 1
source: 《数据分析与知识发现》
site: 万方
title: 嵌入意图识别的医疗健康问答文本语义分类模型
created_at: 2025-09-17 14:08:17.331140
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGgh4aHdqazN4dA%3D%3D

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

parsed_summary:
  {"background": "\u4e2d\u6587\u533b\u7597\u5065\u5eb7\u670d\u52a1\u4e2d\u7684\u81ea\u52a8\u95ee\u7b54\u7cfb\u7edf\u4e9f\u9700\u63d0\u9ad8\u8bed\u4e49\u76f8\u4f3c\u5ea6\u5206\u7c7b\u7684\u51c6\u786e\u6027\uff0c\u4ee5\u4fbf\u4e3a\u7528\u6237\u63d0\u4f9b\u66f4\u7cbe\u51c6\u7684\u7b54\u6848\u5339\u914d\u3002", "contribution": "\u63d0\u51fa\u4e86\u5c06\u610f\u56fe\u8bc6\u522b\u5d4c\u5165\u5230\u8bed\u4e49\u5206\u7c7b\u4e2d\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u6709\u6548\u6539\u5584\u533b\u7597\u9886\u57df\u4e2d\u6587\u81ea\u52a8\u95ee\u7b54\u7684\u51c6\u786e\u7387\uff0c\u4e3a\u667a\u80fd\u5065\u5eb7\u670d\u52a1\u63d0\u4f9b\u4e86\u65b0\u7684\u6280\u672f\u9014\u5f84\u3002", "method": "\u91c7\u7528BERT\u4e0e\u5377\u79ef\u795e\u7ecf\u7f51\u7edc(CNN)\u878d\u5408\uff0c\u5148\u8fdb\u884c\u610f\u56fe\u8bc6\u522b\uff0c\u518d\u5c06\u610f\u56fe\u8bc6\u522b\u6a21\u578b\u4f5c\u4e3a\u5d4c\u5165\u6a21\u5757\u4e0e\u5b6a\u751fBERT\u7ed3\u5408\uff0c\u6784\u5efaITBERT\u8bed\u4e49\u5206\u7c7b\u6a21\u578b\u3002", "objective": "\u6784\u5efa\u878d\u5408\u610f\u56fe\u8bc6\u522b\u673a\u5236\u7684\u8bed\u4e49\u5206\u7c7b\u6a21\u578b\uff0c\u63d0\u5347\u533b\u7597\u5065\u5eb7\u95ee\u7b54\u7cfb\u7edf\u7684\u7b54\u6848\u5339\u914d\u51c6\u786e\u7387\u3002", "results": "\u878d\u5408\u6a21\u578b\u5728\u610f\u56fe\u8bc6\u522b\u51c6\u786e\u7387\u548c\u8bed\u4e49\u76f8\u4f3c\u5ea6\u5206\u7c7bAUC\u503c\u4e0a\u5747\u4f18\u4e8e\u5355\u4e00\u6a21\u578b\uff0c\u663e\u8457\u63d0\u5347\u533b\u7597\u5065\u5eb7\u95ee\u7b54\u7cfb\u7edf\u7684\u6027\u80fd\u3002"}


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: 2025-09-17 14:08:17.331632
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoIeGh3amszeHQ%3D

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

parsed_summary:
  {"background": "\u73b0\u6709\u7684\u60c5\u611f\u5206\u7c7b\u65b9\u6cd5\u666e\u904d\u672a\u5145\u5206\u8003\u8651\u7528\u6237\u4e2a\u6027\u5316\u7279\u5f81\uff0c\u5e76\u5ffd\u7565\u4e86\u8bc4\u8bba\u65f6\u95f4\u7b49\u65f6\u95f4\u56e0\u7d20\u5bf9\u60c5\u611f\u5206\u7c7b\u7ed3\u679c\u7684\u6f5c\u5728\u5f71\u54cd\uff0c\u4e14\u9ad8\u8d28\u91cf\u6807\u6ce8\u6570\u636e\u7a00\u7f3a\u3002", "contribution": "\u8be5\u7814\u7a76\u521b\u65b0\u6027\u5730\u5c06\u77e5\u8bc6\u84b8\u998f\u3001\u7528\u6237\u5386\u53f2\u4fe1\u606f\u4e0e\u65f6\u95f4\u56e0\u7d20\u76f8\u7ed3\u5408\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u6709\u6548\u63d0\u5347\u60c5\u611f\u5206\u7c7b\u6027\u80fd\u7684\u65b0\u65b9\u6cd5\uff0c\u5c24\u5176\u6539\u5584\u4e86\u6a21\u578b\u5bf9\u51b7\u542f\u52a8\u7528\u6237\u7684\u6cdb\u5316\u80fd\u529b\u3002", "method": "\u5229\u7528RoFormer-Sim\u751f\u6210\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u6570\u636e\u589e\u5f3a\uff0c\u5f15\u5165\u8bc4\u8bba\u65f6\u95f4\u5c5e\u6027\u53ca\u7528\u6237\u5386\u53f2\u8bc4\u8bba\u4fe1\u606f\uff0c\u6784\u5efa\u591a\u7279\u5f81\u878d\u5408\u60c5\u611f\u5f97\u5206\u9884\u6d4b\u6a21\u578b\uff0c\u5e76\u7ed3\u5408\u77e5\u8bc6\u84b8\u998f\u65b9\u6cd5\uff0c\u901a\u8fc7SKEP\u6a21\u578b\u5bf9\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u901a\u7528\u6027\u589e\u5f3a\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u878d\u5408\u77e5\u8bc6\u84b8\u998f\u548c\u8bc4\u8bba\u65f6\u95f4\u7684\u65b0\u60c5\u611f\u5206\u7c7b\u65b9\u6cd5\uff0c\u4ee5\u63d0\u5347\u6a21\u578b\u5bf9\u4e2a\u6027\u5316\u548c\u65f6\u95f4\u654f\u611f\u6027\u7684\u8003\u8651\uff0c\u5e76\u6539\u5584\u51b7\u542f\u52a8\u7528\u6237\u7684\u6cdb\u5316\u80fd\u529b\u3002", "results": "\u5728\u4e2d\u6587\u80a1\u5427\u771f\u5b9e\u6570\u636e\u96c6\u5b9e\u9a8c\u4e2d\uff0c\u65b0\u65b9\u6cd5\u5728\u51c6\u786e\u7387\u548cF1\u503c\u4e0a\u5747\u4f18\u4e8eSKEP\u548cELECTRA\u7b49\u65b9\u6cd5\uff0c\u5206\u522b\u5728\u51c6\u786e\u7387\u4e0a\u63d0\u5347\u4e863.1%\u548c0.9%\uff0c\u5728F1\u503c\u4e0a\u63d0\u5347\u4e862.7%\u548c1.0%\u3002"}


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: 2025-09-17 14:08:17.332107
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwMxoIeGh3amszeHQ%3D

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

parsed_summary:
  {"background": "\u65b0\u95fb\u4e3b\u9898\u6587\u672c\u5185\u5bb9\u77ed\u5c0f\u4f46\u4fe1\u606f\u5bc6\u96c6\uff0c\u4f20\u7edf\u5206\u7c7b\u65b9\u6cd5\u901a\u5e38\u53ea\u5173\u6ce8\u5355\u4e00\u7c92\u5ea6\uff08\u8bcd\u6216\u53e5\uff09\u7684\u7279\u5f81\u8868\u793a\uff0c\u672a\u80fd\u5145\u5206\u6316\u6398\u6587\u672c\u591a\u7c92\u5ea6\u95f4\u7684\u5173\u8054\u4fe1\u606f\u3002", "contribution": "\u8be5\u8bba\u6587\u521b\u65b0\u6027\u5730\u7ed3\u5408\u4e86XLNet\u548c\u591a\u7c92\u5ea6\u5bf9\u6bd4\u5b66\u4e60\uff0c\u63d0\u5347\u4e86\u6587\u672c\u5206\u7c7b\u6a21\u578b\u5bf9\u65b0\u95fb\u4e3b\u9898\u8bed\u4e49\u7684\u8868\u8fbe\u80fd\u529b\u548c\u5206\u7c7b\u6027\u80fd\uff0c\u4e3a\u65b0\u95fb\u6587\u672c\u81ea\u52a8\u5206\u7c7b\u63d0\u4f9b\u4e86\u65b0\u601d\u8def\u548c\u9ad8\u6548\u65b9\u6cd5\u3002", "method": "\u91c7\u7528XLNet\u63d0\u53d6\u65b0\u95fb\u6587\u672c\u7684\u8bcd\u7c92\u5ea6\u548c\u53e5\u7c92\u5ea6\u7279\u5f81\uff0c\u5e76\u5229\u7528R-Drop\u5bf9\u6bd4\u5b66\u4e60\u7b56\u7565\uff0c\u5bf9\u591a\u7c92\u5ea6\u7279\u5f81\u6837\u672c\u8fdb\u884c\u76f8\u4f3c\u5ea6\u5b66\u4e60\uff0c\u4ee5\u6316\u6398\u7c92\u5ea6\u95f4\u7684\u6df1\u5c42\u5173\u8054\u3002", "objective": "\u63d0\u51fa\u5e76\u9a8c\u8bc1\u4e00\u79cd\u878d\u5408\u8bcd\u7c92\u5ea6\u548c\u53e5\u7c92\u5ea6\u7279\u5f81\uff0c\u901a\u8fc7\u5bf9\u6bd4\u5b66\u4e60\u6df1\u5316\u7c92\u5ea6\u95f4\u4f9d\u8d56\u5173\u7cfb\u7684\u65b0\u95fb\u4e3b\u9898\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u63d0\u5347\u5206\u7c7b\u7cbe\u5ea6\u3002", "results": "\u5728THUCNews\u3001Toutiao\u548cSHNews\u4e09\u5927\u4e2d\u6587\u65b0\u95fb\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u663e\u793a\uff0c\u63d0\u51fa\u7684\u65b9\u6cd5\u5728\u51c6\u786e\u7387\u548cF1\u503c\u65b9\u9762\u5747\u4f18\u4e8e\u57fa\u51c6\u6a21\u578b\uff0cF1\u503c\u5206\u522b\u4e3a93.88\uff05\u300190.08\uff05\u548c87.35\uff05\uff0c\u9a8c\u8bc1\u4e86\u65b9\u6cd5\u7684\u6709\u6548\u6027\u3002"}


author: 王旭阳;卢世红
date: 2025年1期
keywords: 多标签文本分类;CompGCN;Transformer;CorNet;标签相关性
quote: 0
source: 《贵州师范大学学报（自然科学版）》
site: 万方
title: 基于标签构建与特征融合的多标签文本分类研究方法
created_at: 2025-09-17 14:08:17.332580
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFGd6c2ZkeHhiLXpyMjAyNTAxMDExGgh4aHdqazN4dA%3D%3D

summary:
  目前存在的多标签文本分类任务算法,对于标签的建模不是很成熟,其中对于标签的依赖性问题,以及标签特征和文本特征的融合程度问题,均缺乏有效的处理方法.为了更有效地利用标签间的依赖关系,以及整合标签特征与文本特征的融合,提出了一种名为CGTCN的多标签文本分类模型.该模型从标签构建和特征融合的角度出发,通过CompGCN建模标签依赖关系,先利用Transformer中的多头交叉注意力机制初步融合标签特征和文本特征,然后再通过CorNet网络进一步捕获标签特征与文本特征之间的相关性,从而得到最终的标签预测.实验结果显示,与基准模型相比,该方法能够有效的提升模型性能,在多标签文本分类任务中取得更好的分类效果.

parsed_summary:
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author: 余明峰;秦永彬;黄瑞章
date: 2025年6期
keywords: 多标签文本分类;对比学习;双注意力;标签注意力;自注意力
quote: 0
source: 《计算机应用》
site: 万方
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: 2025-09-17 14:08:17.333082
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzanl5MjAyNTA2MDA1Ggh4aHdqazN4dA%3D%3D

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 Volume Ⅰ)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: 彭鹏;徐红姣
date: 2025年2期
keywords: 开源科技情报;文本分类;信息过滤;预训练语言模型
quote: 0
source: 《数字图书馆论坛》
site: 万方
title: 面向开源科技情报分析的智能文本分类方法研究
created_at: 2025-09-17 14:08:17.333555
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEHN6dHNnbHQyMDI1MDIwMDcaCHhod2prM3h0

summary:
  随着网络信息的爆发式增长,从海量的网络文本信息中识别有价值的科技情报并对其进行智能分类成为开源科技情报分析的关键.针对开源科技情报文本的特点,构建了面向开源科技情报分析的文本智能去噪与分类一体化模型.结合大语言模型与提示工程的自动标注方法进行噪声数据标注及文本分类数据标注;基于预训练语言模型进行噪声识别与过滤,过滤非科技情报文本;利用多语言预训练模型及蒸馏技术,改进损失函数设计,解决类别分布不均和数据不足的问题,实现在一定程度上提升多标签科技情报文本分类的精度和稳定性的目标.实验结果表明,与TextCNN与BERT方法相比,所提出的方法具有较高的分类性能、更好的鲁棒性和适应性.

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author: 武子轩;王烨;于洪
date: 2025年2期
keywords: 层次多标签文本分类;多尺度特征提取;对比学习;层次Micro-F1;层次Macro-F1
quote: 1
source: 《郑州大学学报（理学版）》
site: 万方
title: 基于多尺度特征提取的层次多标签文本分类方法
created_at: 2025-09-17 14:08:17.334027
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3p6ZHh4YjIwMjUwMjAwNBoIeGh3amszeHQ%3D

summary:
  针对现有的特征提取方法忽略文本局部和全局联系的问题,提出了基于多尺度特征提取的层次多标签文本分类方法.首先,设计了多尺度特征提取模块,对不同尺度特征进行捕捉,更好地表示文本语义.其次,将层次特征嵌入文本表示中,得到具有标签特征的文本语义表示.最后,在标签层次结构的指导下对输入文本构建正负样本,进行对比学习,提高分类效果.在WOS、RCV1-V2、NYT和AAPD数据集上进行对比实验,结果表明,所提模型在评价指标上表现出色,超过了其他主流模型.此外,针对层次分类提出层次Micro-F1和层次Macro-F1指标,并对模型效果进行了评价.

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author: 郑诚;李鹏飞
date: 2025年6期
keywords: 文本分类;超图;特征融合;SenticNet词库;自然语言处理
quote: 0
source: 《计算机工程》
site: 万方
title: 基于双超图神经网络特征融合的文本分类
created_at: 2025-09-17 14:08:17.334518
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmpzamdjMjAyNTA2MDEzGgh4aHdqazN4dA%3D%3D

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: 熊亿坤;付雪峰;盛黄煜
date: 2025年1期
keywords: BERT模型;基于卷积神经网络的文本分类模型;注意力机制;新闻文本分类
quote: 0
source: 《江西师范大学学报（自然科学版）》
site: 万方
title: 一种融合BERT和注意力机制的新闻文本分类方法
created_at: 2025-09-17 14:08:17.335000
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWp4c2ZkeHhiMjAyNTAxMDA3Ggh4aHdqazN4dA%3D%3D

summary:
  文本分类任务是在自然语言处理中的一项重要任务,旨在将给定的文本分配到预定义的不同类别或标签中.针对近年来备受关注的注意力机制和一种基于Transformer结构的预训练模型BERT,该文提出一种基于BERT-TextRCNN-Attention混合神经网络的新闻文本分类方法.为了增强文本的特征表示和模型的分类效果,该分类方法首先使用BERT预训练模型对文本进行预训练,并作为TextRCNN的词向量嵌入,其次采用TextRCNN模型和注意力机制进一步对文本的上下文特征和局部关键特征进行提取,再对新闻文本进行分类;最后在THUCNews数据集上进行对比实验.实验结果表明:该文提出方法在准确率和F,值上比Transformer、TextRNN、TextCNN、DPCNN等文本分类模型均有所提升.

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author: 陈中涛;周亚同
date: 2025年4期
keywords: 弱监督;文本分类;自训练;种子词
quote: 0
source: 《计算机工程与应用》
site: 万方
title: 简单且有效的弱监督中文文本分类算法
created_at: 2025-09-17 14:08:17.335489
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWpzamdjeXl5MjAyNTA0MDE3Ggh4aHdqazN4dA%3D%3D

summary:
  目前基于种子词的弱监督文本分类算法大多需要从数据集中搜索所有种子词并以此扩展类别词典,出现频率较低的种子词的类别识别能力也较低.因此设计了一个简单且有效的弱监督中文文本分类算法(simple and effective weakly supervised Chinese text classification,SEWClass).该方法利用预训练语言模型初始权重生成对文本的抽象理解,并以此为基础继续生成抽象约束条件和具象约束条件,以构建初次训练的伪标签数据;根据类别数量联合构建降维模型与分类器,以适应弱监督文本分类需要预先指定类别和在自训练过程中需要增加训练数据的特点;通过两种约束条件,伪标签数据拥有较高精确率,并在自训练过程中仅训练降维模型以提升召回率和算法效率.SEWClass对每个类别只需要一个种子词,如类别名称,即可完成分类任务,且SEWClass的性能与种子词是否出现在数据集中无关.SEWClass在THUCNews与toutiao两个中文数据集上的性能均远高于其他弱监督算法.

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author: Benamar Hamzaoui;Djelloul Bouchiha;Abdelghani Bouziane
date: 2025
original_link: https://ieeexplore.ieee.org/document/11126481/
site: ieeeXplore
title: Hierarchical Arabic Text Classification: BERT-ANN vs. BERT-BiLSTM
created_at: 2025-09-17 14:08:17.338913
summary:
  Text classification, the task of assigning text to predefined categories, is a fundamental area in natural language processing, often requiring sophisticated techniques and systems. Hierarchical text classification, which structures categories in a tree-like hierarchy, introduces additional challenges due to the interplay of similarities and relationships between categories across different levels. The application of artificial intelligence (AI) techniques has significantly advanced text classification, but addressing this task in Arabic remains particularly challenging due to limited linguistic resources. In this paper, we propose and evaluate two AI-based models, BERT-ANN and BERT-BiLSTM, for hierarchical Arabic text classification. Experimental results reveal that the BERT-ANN-based model achieves superior performance on short text classification, obtaining an HF-score of 92%, compared to 77% achieved by the BERT-BiLSTM-based model.

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author: Songsong Liu;Haijun Tao;Shiling Feng
date: 2019
original_link: https://ieeexplore.ieee.org/document/8996183/
site: ieeeXplore
title: Text Classification Research Based on Bert Model and Bayesian Network
created_at: 2025-09-17 14:08:17.339405
summary:
  The Bert model is a pre-training model based on deep learning. It has refreshed the best performance of 11 NLP missions as soon as it appears, and it also has a wide range of applications. The text data of people's livelihood governance is huge, and there is a large amount of unstructured data, which makes the traditional text analysis and mining technology increase in the space-time complexity of computing; so it is very important to choose appropriate text classification technology. Here we propose to use the Bert model in combination with the Bayesian network to achieve one new classification of text. That is, the Bayesian network is used to perform the classification of two categories first, and we can get the approximate category range of each text, and then the Bert model is used to classify the text into specific categories. The combination of these two methods can greatly reduce the errors caused by the classification defects of using only one of the methods. Thereby achieving an improvement in the accuracy of text classification.

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author: AIT YAHIA Ikram;LOQMAN Chakir
date: 2019
original_link: https://ieeexplore.ieee.org/document/8942343/
site: ieeeXplore
title: Arabic Text Classification in the Legal Domain
created_at: 2025-09-17 14:08:17.339898
summary:
  The field of law in Morocco is behind on progress to other fields, where the archiving of legal documents is done manually and the access to the information is increasingly complex. In the scope of this work, we investigate the application of text classification methods to support law professionals, by proposing a system able to automatically select the class of a judgement. We will build four models which are Naïve Bayes, K-Nearest Neighbor, Support Vector Machine and Decision Tree to classify Arabic legal documents, which are tried by the Moroccan court.

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author: Sichao Wei;Jianyi Guo;Zhengtao Yu;Peng Chen;Yantuan Xian
date: 2013
original_link: https://ieeexplore.ieee.org/document/6561863/
site: ieeeXplore
title: The instructional design of Chinese text classification based on SVM
created_at: 2025-09-17 14:08:17.340372
summary:
  In order to resolve the comprehension difficulties of theory and implementation about Chinese text classification in " The principle and application of pattern recognition" curriculum for graduate students, this paper introduces the experiment of Chinese text classification into teaching practice. According to the text classification characteristics, we design the experiment scheme about Chinese text classification based on SVM, using word frequency statistics to extract feature and SVM classification algorithm, using vector space model to construct the feature space of text classification. So that readers can deeply understand and master the theoretical knowledge through the open the link, then expand on this basis.

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author: Jun Zhu
date: 2023
original_link: https://ieeexplore.ieee.org/document/10280844/
site: ieeeXplore
title: Classification Research Based on Quantitative Expansion of Short Text Feature Correlation
created_at: 2025-09-17 14:08:17.340845
summary:
  Short text categorization is one of the important problems in Natural language processing. Due to the sparsity, eal-time, and non standardization of short text features, traditional text classification algorithms cannot effectively classify short texts. Feature vectors are commonly used forms of representation, but due to the limited length of short text, feature vectors often cannot accurately express short text information. Therefore, in order to improve the accuracy and reliability of classification, we propose a short text feature correlation quantification extended classification algorithm called IMIR. The IMIR algorithm is based on the idea of distinguishing features by class. By calculating the correlation between features, we can expand the feature vector and obtain more relevant information. After experimental verification, this method can significantly improve accuracy in short text classification. By introducing the IMIR algorithm, we can fully utilize the correlation between short texts and enhance the expression ability of feature vectors.

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author: Yujia Zhai;Wei Song;Xianjun Liu;Lizhen Liu;Xinlei Zhao
date: 2018
original_link: https://ieeexplore.ieee.org/document/8663882/
site: ieeeXplore
title: A Chi-Square Statistics Based Feature Selection Method in Text Classification
created_at: 2025-09-17 14:08:17.341336
summary:
  Text classification refers to the process of automatically determining text categories based on text content in a given classification system. Text classification mainly includes several steps such as word segmentation, feature selection, weight calculation and classification performance evaluation. Among them, feature selection is a key step in text classification, which affects the classification accuracy. Feature selection can help indicate the relevance of text contents and can better classify the text. Meanwhile feature selection has a great influence on the classification result. Text classification is a very important module in text processing, and it is widely applied in areas like spam filtering, news classification, sentiment classification, and part-of-speech tagging. This paper proposes a method for extracting feature words based on Chi-square Statistics. Because the feature words that appear together or separately may differ in different situations, we classify texts by using single word and double words as features at the same time. Based on our method, we performed experiments using classical Naive Bayes and Support Vector Machine classification algorithms. The efficiency of our method was demonstrated by the comparison and analysis of experimental results.

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author: Chan Huang;Yanmei Luo;Qingyuan Li
date: 2019
original_link: https://ieeexplore.ieee.org/document/9023722/
site: ieeeXplore
title: Text Classification Method Based on Co-Occurrence Events
created_at: 2025-09-17 14:08:17.341815
summary:
  In view of the problem that emergency events are to be reported only following with the time sequence and theme types tend to be difficult to be distinguished from the texts. This paper first attempt to describe the reporting texts through events, in accordance with co-occurrence events appear in the texts, then endeavors to explore the properties such as distance and correlation between co-occurrence events in the text on the basis of the semantic relations between events to cluster the high similarity texts and eventually achieves text classification. Experiment shows that combining semantic relations between events with the relationship between co-occurrence events are more effective for text categorization.

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author: Heng Zhang;Chengzhi Zhang
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12955
pdf_url: https://arxiv.org/pdf/2509.12955
site: arxiv
title: Automated Generation of Research Workflows from Academic Papers: A Full-text Mining Framework
created_at: 2025-09-17 14:08:17.342288
summary:
  Abstract:The automated generation of research workflows is essential for improving the reproducibility of research and accelerating the paradigm of "AI for Science". However, existing methods typically extract merely fragmented procedural components and thus fail to capture complete research workflows. To address this gap, we propose an end-to-end framework that generates comprehensive, structured research workflows by mining full-text academic papers. As a case study in the Natural Language Processing (NLP) domain, our paragraph-centric approach first employs Positive-Unlabeled (PU) Learning with SciBERT to identify workflow-descriptive paragraphs, achieving an F1-score of 0.9772. Subsequently, we utilize Flan-T5 with prompt learning to generate workflow phrases from these paragraphs, yielding ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.4543, 0.2877, and 0.4427, respectively. These phrases are then systematically categorized into data preparation, data processing, and data analysis stages using ChatGPT with few-shot learning, achieving a classification precision of 0.958. By mapping categorized phrases to their document locations in the documents, we finally generate readable visual flowcharts of the entire research workflows. This approach facilitates the analysis of workflows derived from an NLP corpus and reveals key methodological shifts over the past two decades, including the increasing emphasis on data analysis and the transition from feature engineering to ablation studies. Our work offers a validated technical framework for automated workflow generation, along with a novel, process-oriented perspective for the empirical investigation of evolving scientific paradigms. Source code and data are available at: this https URL.

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author: Allen Schmaltz
date: 16 September, 2025
original_link: https://arxiv.org/abs/2509.12760
pdf_url: https://arxiv.org/pdf/2509.12760
site: arxiv
title: Similarity-Distance-Magnitude Activations
created_at: 2025-09-17 14:08:17.342799
summary:
  Abstract:We introduce a more robust and interpretable formulation of the standard softmax activation function commonly used with neural networks by adding Similarity (i.e., correctly predicted depth-matches into training) awareness and Distance-to-training-distribution awareness to the existing output Magnitude (i.e., decision-boundary) awareness. When used as the final-layer activation with language models, the resulting Similarity-Distance-Magnitude (SDM) activation function is more robust than the softmax function to co-variate shifts and out-of-distribution inputs in high-probability regions, and provides interpretability-by-exemplar via dense matching. Complementing the prediction-conditional estimates, the SDM activation enables a partitioning of the class-wise empirical CDFs to guard against low class-wise recall among selective classifications. These properties make it preferable for selective classification, even when considering post-hoc calibration methods over the softmax.

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author: Anmol Singhal Navya Singhal
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12611
pdf_url: https://arxiv.org/pdf/2509.12611
site: arxiv
created_at: 2025-09-17 14:08:17.343265
summary:
  Abstract:Financial news sentiment analysis is crucial for anticipating market movements. With the rise of AI techniques such as Large Language Models (LLMs), which demonstrate strong text understanding capabilities, there has been renewed interest in enhancing these systems. Existing methods, however, often struggle to capture the complex economic context of news and lack transparent reasoning, which undermines their reliability. We propose Analogy-Driven Financial Chain-of-Thought (AD-FCoT), a prompting framework that integrates analogical reasoning with chain-of-thought (CoT) prompting for sentiment prediction on historical financial news. AD-FCoT guides LLMs to draw parallels between new events and relevant historical scenarios with known outcomes, embedding these analogies into a structured, step-by-step reasoning chain. To our knowledge, this is among the first approaches to explicitly combine analogical examples with CoT reasoning in finance. Operating purely through prompting, AD-FCoT requires no additional training data or fine-tuning and leverages the model's internal financial knowledge to generate rationales that mirror human analytical reasoning. Experiments on thousands of news articles show that AD-FCoT outperforms strong baselines in sentiment classification accuracy and achieves substantially higher correlation with market returns. Its generated explanations also align with domain expertise, providing interpretable insights suitable for real-world financial analysis.

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


author: Yingxin Lai;Zitong Yu;Jun Wang;Linlin Shen;Yong Xu;Xiaochun Cao
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12546
pdf_url: https://arxiv.org/pdf/2509.12546
site: arxiv
title: Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection
created_at: 2025-09-17 14:08:17.343759
summary:
  Abstract:Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy,which we attribute to the ecological invalidity of training this http URL work introduces Agent4FaceForgery to address two fundamental problems: (1) how to capture the diverse intents and iterative processes of human forgery creation, and (2) how to model the complex, often adversarial, text-image interactions that accompany forgeries in social media. To solve this,we propose a multi-agent framework where LLM-poweredagents, equipped with profile and memory modules, simulate the forgery creation process. Crucially, these agents interact in a simulated social environment to generate samples labeled for nuanced text-image consistency, moving beyond simple binary classification. An Adaptive Rejection Sampling (ARS) mechanism ensures data quality and diversity. Extensive experiments validate that the data generated by our simulationdriven approach brings significant performance gains to detectors of multiple architectures, fully demonstrating the effectiveness and value of our framework.

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author: Jiayi He;Yangmin Huang;Qianyun Du;Xiangying Zhou;Zhiyang He;Jiaxue Hu;Xiaodong Tao;Lixian Lai
date: 15 September, 2025
original_link: https://arxiv.org/abs/2509.12440
pdf_url: https://arxiv.org/pdf/2509.12440
site: arxiv
created_at: 2025-09-17 14:08:17.344239
summary:
  Abstract:The increasing deployment of Large Language Models (LLMs) in healthcare necessitates a rigorous evaluation of their factual reliability. However, existing benchmarks are often limited by narrow domains of data, failing to capture the complexity of real-world medical information. To address this critical gap, we introduce MedFact, a new and challenging benchmark for Chinese medical fact-checking. MedFact comprises 2,116 expert-annotated instances curated from diverse real-world texts, spanning 13 medical specialties, 8 fine-grained error types, 4 writing styles, and multiple difficulty levels. Its construction employs a hybrid AI-human framework where iterative expert feedback refines an AI-driven, multi-criteria filtering process, ensuring both high data quality and difficulty. We conduct a comprehensive evaluation of 20 leading LLMs, benchmarking their performance on veracity classification and error localization against a human expert baseline. Our results reveal that while models can often determine if a text contains an error, precisely localizing it remains a substantial challenge, with even top-performing models falling short of human performance. Furthermore, our analysis uncovers a frequent ``over-criticism'' phenomenon, a tendency for models to misidentify correct information as erroneous, which is exacerbated by advanced reasoning techniques such as multi-agent collaboration and inference-time scaling. By highlighting these critical challenges for deploying LLMs in medical applications, MedFact provides a robust resource to drive the development of more factually reliable and medically aware models.

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


date: 13 September, 2025
original_link: https://arxiv.org/abs/2509.10882
pdf_url: https://arxiv.org/pdf/2509.10882
site: arxiv
title: Term2Note: Synthesising Differentially Private Clinical Notes from Medical Terms
created_at: 2025-09-17 14:08:17.347672
author:
  Yuping Wu;Viktor Schlegel;Warren Del-Pinto;Srinivasan Nandakumar;Iqra Zahid;Yidan Sun;Usama Farghaly Omar;Amirah Jasmine;Arun-Kumar Kaliya-Perumal;Chun Shen Tham;Gabriel Connors;Anil A Bharath;Goran Nenadic

summary:
  Abstract:Training data is fundamental to the success of modern machine learning models, yet in high-stakes domains such as healthcare, the use of real-world training data is severely constrained by concerns over privacy leakage. A promising solution to this challenge is the use of differentially private (DP) synthetic data, which offers formal privacy guarantees while maintaining data utility. However, striking the right balance between privacy protection and utility remains challenging in clinical note synthesis, given its domain specificity and the complexity of long-form text generation. In this paper, we present Term2Note, a methodology to synthesise long clinical notes under strong DP constraints. By structurally separating content and form, Term2Note generates section-wise note content conditioned on DP medical terms, with each governed by separate DP constraints. A DP quality maximiser further enhances synthetic notes by selecting high-quality outputs. Experimental results show that Term2Note produces synthetic notes with statistical properties closely aligned with real clinical notes, demonstrating strong fidelity. In addition, multi-label classification models trained on these synthetic notes perform comparably to those trained on real data, confirming their high utility. Compared to existing DP text generation baselines, Term2Note achieves substantial improvements in both fidelity and utility while operating under fewer assumptions, suggesting its potential as a viable privacy-preserving alternative to using sensitive clinical notes.

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author: Alexandru Chirvasitu
date: 13 September, 2025
original_link: https://arxiv.org/abs/2509.10812
pdf_url: https://arxiv.org/pdf/2509.10812
site: arxiv
title: Algebra bundles, projective flatness and rationally-deformed tori
created_at: 2025-09-17 14:08:17.348161
summary:
  Abstract:We show that isomorphism classes [\mathcal{A}] of flat q\times q matrix bundles \mathcal{A} (or projectively flat rank-q complex vector bundles \mathcal{E}) on a pro-torus \mathbb{T} are in bijective correspondence with the Čech cohomology group H^2(\mathbb{T},\mu_q:=\text{$q^{th}$ roots of unity}) (respectively H^2(\mathbb{Z})) via the image of [A]\in H^1(\mathbb{T},PGL(q,\mathcal{C}_{\mathbb{T}})) through H^1(\mathbb{T},PGL(q,\mathcal{C}_{\mathbb{T}}))\xrightarrow{\quad}H^2(\mathbb{T},\mu(q,\mathcal{C}_{\mathbb{T}})) (respectively the first Chern class c_1(\mathcal{E})). This is in the spirit of Auslander-Szczarba's result identifying real flat bundles on the torus with their first two Stiefel-Whitney classes, and contrasts with classifying spaces B\Gamma of compact Lie groups \Gamma (as opposed to \mathbb{T}^n\cong B\mathbb{Z}^n), on which flat non-trivial vector bundles abound. The discussion both recovers the Disney-Elliott-Kumjian-Raeburn classification of rational non-commutative tori \mathbb{T}^n_{\theta} with a different, bundle-theoretic proof, and sheds some light on the connection between topological invariants associated to \mathbb{T}^2_{\theta}, \theta\in\mathbb{Q} by Rieffel and respectively Høegh-Krohn-Skjelbred.

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author: Flottorp SA, Oxman AD, Krause J, Musila NR, Wensing M, Godycki-Cwirko M, Baker R, Eccles MP.
original_link: https://pubmed.ncbi.nlm.nih.gov/23522377/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC3617095/pdf/1748-5908-8-35.pdf
site: pubmed
created_at: 2025-09-17 14:08:17.351635
summary:
  Background: Determinants of practice are factors that might prevent or enable improvements. Several checklists, frameworks, taxonomies, and classifications of determinants of healthcare professional practice have been published. In this paper, we describe the development of a comprehensive, integrated checklist of determinants of practice (the TICD checklist).

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title:
  A checklist for identifying determinants of practice: a systematic review and synthesis of frameworks and taxonomies of factors that prevent or enable improvements in healthcare professional practice.


author: Howe CJ, Barbrook AC, Spencer M, Robinson P, Bordalejo B, Mooney LR.
original_link: https://pubmed.ncbi.nlm.nih.gov/11226609/
site: pubmed
title: Manuscript evolution.
created_at: 2025-09-17 14:08:17.352114
summary:
  Frequently, letters, words and sentences are used in undergraduate textbooks and the popular press as an analogy for the coding, transfer and corruption of information in DNA. We discuss here how the converse can be exploited, by using programs designed for biological analysis of sequence evolution to uncover the relationships between different manuscript versions of a text. We point out similarities between the evolution of DNA and the evolution of texts.

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author: Shatkay H, Pan F, Rzhetsky A, Wilbur WJ.
original_link: https://pubmed.ncbi.nlm.nih.gov/18718948/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC2530883/pdf/btn381.pdf
site: pubmed
created_at: 2025-09-17 14:08:17.352607
summary:
  Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no 'average biologist' client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks.

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title:
  Multi-dimensional classification of biomedical text: toward automated, practical provision of high-utility text to diverse users.


author: Odendaal WA, Anstey Watkins J, Leon N, Goudge J, Griffiths F, Tomlinson M, Daniels K.
original_link: https://pubmed.ncbi.nlm.nih.gov/32216074/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7098082/pdf/CD011942.pdf
site: pubmed
created_at: 2025-09-17 14:08:17.353098
summary:
  Background: Mobile health (mHealth), refers to healthcare practices supported by mobile devices, such as mobile phones and tablets. Within primary care, health workers often use mobile devices to register clients, track their health, and make decisions about care, as well as to communicate with clients and other health workers. An understanding of how health workers relate to, and experience mHealth, can help in its implementation.

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title:
  Health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis.


author: Liu T, Meyerhoff J, Eichstaedt JC, Karr CJ, Kaiser SM, Kording KP, Mohr DC, Ungar LH.
original_link: https://pubmed.ncbi.nlm.nih.gov/34963643/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8912980/pdf/nihms-1775906.pdf
site: pubmed
title: The relationship between text message sentiment and self-reported depression.
created_at: 2025-09-17 14:08:17.353584
summary:
  Background: Personal sensing has shown promise for detecting behavioral correlates of depression, but there is little work examining personal sensing of cognitive and affective states. Digital language, particularly through personal text messages, is one source that can measure these markers.

parsed_summary:
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original_link: https://pubmed.ncbi.nlm.nih.gov/30968198/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC6534531/pdf/285_2019_Article_1332.pdf
site: pubmed
title: Best match graphs.
created_at: 2025-09-17 14:08:17.354056
author:
  Geiß M, Chávez E, González Laffitte M, López Sánchez A, Stadler BMR, Valdivia DI, Hellmuth M, Hernández Rosales M, Stadler PF.

summary:
  Best match graphs arise naturally as the first processing intermediate in algorithms for orthology detection. Let T be a phylogenetic (gene) tree T and [Formula: see text] an assignment of leaves of T to species. The best match graph [Formula: see text] is a digraph that contains an arc from x to y if the genes x and y reside in different species and y is one of possibly many (evolutionary) closest relatives of x compared to all other genes contained in the species [Formula: see text]. Here, we characterize best match graphs and show that it can be decided in cubic time and quadratic space whether [Formula: see text] derived from a tree in this manner. If the answer is affirmative, there is a unique least resolved tree that explains [Formula: see text], which can also be constructed in cubic time.

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author: Tlachac ML, Shrestha A, Shah M, Litterer B, Rundensteiner EA.
original_link: https://pubmed.ncbi.nlm.nih.gov/36044503/
site: pubmed
title: Automated Construction of Lexicons to Improve Depression Screening With Text Messages.
created_at: 2025-09-17 14:08:17.354550
summary:
  Given that depression is one of the most prevalent mental illnesses, developing effective and unobtrusive diagnosis tools is of great importance. Recent work that screens for depression with text messages leverage models relying on lexical category features. Given the colloquial nature of text messages, the performance of these models may be limited by formal lexicons. We thus propose a strategy to automatically construct alternative lexicons that contain more relevant and colloquial terms. Specifically, we generate 36 lexicons from fiction, forum, and news corpuses. These lexicons are then used to extract lexical category features from the text messages. We utilize machine learning models to compare the depression screening capabilities of these lexical category features. Out of our 36 constructed lexicons, 14 achieved statistically significantly higher average F1 scores over the pre-existing formal lexicon and basic bag-of-words approach. In comparison to the pre-existing lexicon, our best performing lexicon increased the average F1 scores by 10%. We thus confirm our hypothesis that less formal lexicons can improve the performance of classification models that screen for depression with text messages. By providing our automatically constructed lexicons, we aid future machine learning research that leverages less formal text.

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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: 2025-09-17 14:08:17.355034
original_link:
  https://www.cqvip.com/doc/journal/3506615314?sign=83b67022f45987e7ef845482f4af85934663dfade1c55d401d7e9b39f7aba614&expireTime=1789625163861&resourceId=3506615314

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

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author: 李晨;刘纳;郑国风;杨杰;道路
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: 2025-09-17 14:08:17.355538
original_link:
  https://www.cqvip.com/doc/journal/3502600080?sign=eeb31a855897d35bfcc6a8131656febe39d2be356c5b6607501636b3f3445a8e&expireTime=1789625163861&resourceId=3502600080

summary:
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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: 2025-09-17 14:08:17.356024
original_link:
  https://www.cqvip.com/doc/journal/3505889201?sign=f476f0323c4bf1f40e57feaca77ce0efb91d3b1913d9e4cbfe265dad988ce4dd&expireTime=1789625163861&resourceId=3505889201

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

parsed_summary:
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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: 2025-09-17 14:08:17.356525
original_link:
  https://www.cqvip.com/doc/journal/3501748678?sign=59acc6cfe2920206f6e24c3a56c557db712e525b452a8f2682599c7887a881d2&expireTime=1789625163861&resourceId=3501748678

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

parsed_summary:
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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: 2025-09-17 14:08:17.357020
original_link:
  https://www.cqvip.com/doc/journal/3500943458?sign=cabf42a2f361765957f69ff4d354f81820081a0e354f66e2b26c093565323b86&expireTime=1789625163861&resourceId=3500943458

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

parsed_summary:
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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: 2025-09-17 14:08:17.357511
original_link:
  https://www.cqvip.com/doc/journal/3502552353?sign=b026c6e9a12ab08fd0a2bcc2306c41a10452e70d62ab1c75ed19cf6cc9184eb0&expireTime=1789625163861&resourceId=3502552353

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

parsed_summary:
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author: 杜润隆;谭柯鑫;高添;韩郅;徐云凤
keywords: TextRCNN;施工方案;Albert;段落文本
source: 水利水电技术(中英文)
site: 维普
title: 施工方案类别及文本分类模型实现研究分类
created_at: 2025-09-17 14:08:17.358005
original_link:
  https://www.cqvip.com/doc/journal/3501769600?sign=bed01dcec63a146d714627b95f6ed8b15d345449a5e65cc34da1f5ccb54402bb&expireTime=1789625163861&resourceId=3501769600

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

parsed_summary:
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author: 曾谁飞;孟瑶;刘静
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: 2025-09-17 14:08:17.358487
original_link:
  https://www.cqvip.com/doc/journal/3501556210?sign=2f042eef480b4094642760059db2c912c5868b86a3c3d2c9510b40a59781d2bf&expireTime=1789625163861&resourceId=3501556210

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

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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: 2025-09-17 14:08:17.358959
original_link:
  https://www.cqvip.com/doc/journal/3483736790?sign=eff96c339907240bcbcc3260384870269550900ec02c132d7dcc521297dcbf76&expireTime=1789625163861&resourceId=3483736790

summary:
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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: 2025-09-17 14:08:17.359467
original_link:
  https://www.cqvip.com/doc/journal/3483995749?sign=025cf47ac38f8ac45b4feed90cc0a0d5fbab4f2409a7f96ab86a756847e15ad6&expireTime=1789625163861&resourceId=3483995749

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author: 廖列法;姜炫至
keywords: 知识蒸馏;预训练模型;文本分类
source: 计算机工程与应用
site: 维普
title: 融合预训练和双级元蒸馏的医学短文本分类方法
created_at: 2025-09-17 14:08:17.359961
original_link:
  https://www.cqvip.com/doc/journal/3483661962?sign=50b58d15c850437aef9d9f0c2c80526c1eebdf4a3f25e1baa63739859b5121d5&expireTime=1789625163861&resourceId=3483661962

summary:
  为解决医学文本使用预训练模型处理速度慢、对硬件的算力要求高、难以分辨少样本类别,以及使用传统小模型由于自身限制难以做到足够的准确度的问题,提出了一种融合预训练和元蒸馏的文本分类模型PTMD(fusion of pre-training and meta distillation model)。PTMD针对医学文本的多标签问题,通过对比训练对RoBERTa预训练方法进行微调,再由双向内置注意力简单循环单元充分获取语义信息。最后在传统蒸馏模型的基础上融合元学习和助教的思想,通过教学实验和双级模型等方法,提高模型的教学水平,最终在减少训练成本的基础上得到一个高性能医学文本分类模型。实验结果表明,教师模型在CHIP2019评测三数据集上的F1值达到了85.47%,同时学生模型在F1值损失1.45个百分点的情况下,将模型规模缩小到教师模型的近1/6,效果高于大多数传统预训练模型和知识蒸馏模型,证明了该模型具有良好的实用价值。

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author: 温浩;杨洋
keywords: 语义强化;深度学习;临床短文本分类;知识图谱;ERNIE
source: 计算机工程与应用
site: 维普
title: 融合ERNIE与知识增强的临床短文本分类研究
created_at: 2025-09-17 14:08:17.360470
original_link:
  https://www.cqvip.com/doc/journal/3483712439?sign=1b2d3224c18eadad1095d04438e3bf8b3e1f529af8f913d38847a7f13c8db38b&expireTime=1789625163861&resourceId=3483712439

summary:
  提出一种引入专业医疗知识与文本独特知识的ERNIE模型用于识别无规则的临床短文本。目前ERNIE模型具有一定的医疗领域知识,但是在处理下游任务时无法引入专业医疗知识与文本独特知识,因此为提高临床短文本分类的精确度与效率,提出KW-ERNIE-BiGRU模型。该模型通过引入医学知识与文本独特知识的ERNIE模型训练文本的特征向量,利用BiGRU强化上下文信息,最终在输出层进行文本分类。通过在真实的临床文本的验证与对比实验,KW-ERNIE-BiGRU模型的精确率、召回率、宏F1分别为93.4%、92.1%、92.7%,均优于其他模型。

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author: 孙海涛;林佳瑜;梁祖红;郭洁
keywords: 标签混淆;数据增强;中文新闻主题;预训练模型;文本分类
source: 计算机应用
site: 维普
title: 结合标签混淆的中文文本分类数据增强技术
created_at: 2025-09-17 14:08:17.360986
original_link:
  https://www.cqvip.com/doc/journal/3483695334?sign=9215e8796a8803aadc56b65c12d263c603d629274f77a2a6c35bd60b5a6589fb&expireTime=1789625163861&resourceId=3483695334

summary:
  传统数据增强技术,如同义词替换、随机插入和随机删除等,可能改变文本的原始语义,甚至导致关键信息丢失。此外,在文本分类任务中,数据通常包含文本部分和标签部分,然而传统数据增强方法仅针对文本部分。为解决这些问题,提出一种结合标签混淆的数据增强(LCDA)技术,从文本和标签这2个基本方面入手,为数据提供全面的强化。在文本方面,通过对文本进行标点符号随机插入和替换以及句末标点符号补齐等增强,在保留全部文本信息和顺序的同时增加文本的多样性;在标签方面,采用标签混淆方法生成模拟标签分布替代传统的one-hot标签分布,以更好地反映实例和标签与标签之间的关系。在THUCNews(TsingHua University Chinese News)和Toutiao这2个中文新闻数据集构建的小样本数据集上分别结合TextCNN、TextRNN、BERT(Bidirectional Encoder Representations from Transformers)和RoBERTa-CNN(Robustly optimized BERT approach Convolutional Neural Network)文本分类模型的实验结果表明,与增强前相比,性能均得到显著提升。其中,在由THUCNews数据集构造的50-THU数据集上,4种模型结合LCDA技术后的准确率相较于增强前分别提高了1.19、6.87、3.21和2.89个百分点;相较于softEDA(Easy Data Augmentation with soft labels)方法增强的模型分别提高了0.78、7.62、1.75和1.28个百分点。通过在文本和标签这2个维度的处理结果可知,LCDA技术能显著提升模型的准确率,在数据量较少的应用场景中表现尤为突出。

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author: 武子轩;王烨;于洪
keywords: 1;层次多标签文本分类;层次Micro-F;层次Macro-F;多尺度特征提取;对比学习
source: 郑州大学学报（理学版）
site: 维普
title: 基于多尺度特征提取的层次多标签文本分类方法
created_at: 2025-09-17 14:08:17.361485
original_link:
  https://www.cqvip.com/doc/journal/3475024013?sign=381efb4e46ca3b34f22f4f425b25875a347f8056a3665e0d1500f1a416747c8b&expireTime=1789625163861&resourceId=3475024013

summary:
  针对现有的特征提取方法忽略文本局部和全局联系的问题,提出了基于多尺度特征提取的层次多标签文本分类方法。首先,设计了多尺度特征提取模块,对不同尺度特征进行捕捉,更好地表示文本语义。其次,将层次特征嵌入文本表示中,得到具有标签特征的文本语义表示。最后,在标签层次结构的指导下对输入文本构建正负样本,进行对比学习,提高分类效果。在WOS、RCV1-V2、NYT和AAPD数据集上进行对比实验,结果表明,所提模型在评价指标上表现出色,超过了其他主流模型。此外,针对层次分类提出层次Micro-F 1和层次Macro-F 1指标,并对模型效果进行了评价。

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author: 陈敏;王雷春;徐瑞;史含笑;徐渺
keywords: XLNet;自然语言处理;对比学习;文本分类;新闻主题
source: 郑州大学学报（理学版）
site: 维普
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: 2025-09-17 14:08:17.361961
original_link:
  https://www.cqvip.com/doc/journal/3474986946?sign=3430022489cff7770251f294e09380604e3c612dd785a686794fd89ac8e907ca&expireTime=1789625163861&resourceId=3474986946

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F 1值上都有更好的表现,在三个数据集上的F 1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: 翟梦鑫;周艳玲;余杭
keywords: 小样本文本分类;问题导向式模板;提示调优
source: 计算机应用研究
site: 维普
title: 基于问题导向式提示调优小样本文本分类
created_at: 2025-09-17 14:08:17.362468
original_link:
  https://www.cqvip.com/doc/journal/3480290043?sign=62b9a8ce5ba78ee62b3b8198f4e2d50ad4bcb6b12ac677041102fcd7162107ee&expireTime=1789625163861&resourceId=3480290043

summary:
  低资源场景下提示调优比带有额外分类器的通用微调方法(fine-tuning)分类性能好,但提示调优中设计一个较好的提示模板和标签词映射器需要花费大量的精力。针对该问题,提出问题导向式提示调优的小样本分类方法(question-oriented prompt-tuning,QPT)。首先,利用数据集标签和可训练的连续提示构建提问形式的模板,通过预训练模型学习到最优提示模板;然后,每条样本用模板进行填充,将文本分类任务转换成完形填空任务;最后,使用外部知识及两种细化方法构建标签词映射器,通过预测的标签词与分类标签的映射关系得出分类结果。在公开数据集AG’s News和IMDB上进行实验,结果表明,该方法在5-shot、10-shot和20-shot任务上性能均有所提升,在5-shot任务上准确率分别提高了0.81和1.36百分点,与基线模型相比,不仅易于实现且性能取得了最优。

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author: 胡婕;郑启扬;曹芝兰;刘梦赤
keywords: 极限多标签文本分类;链接策略;细粒度特征;感知文本
source: 中文信息学报
site: 维普
title: 基于链接策略和不同粒度特征融合的极限多标签文本分类模型
created_at: 2025-09-17 14:08:17.362954
original_link:
  https://www.cqvip.com/doc/journal/3484086182?sign=06869610193e1e46de0286131202d73d343346b85dec514e6b2dbf03e541f944&expireTime=1789625163861&resourceId=3484086182

summary:
  现有基于Transformer的极限多标签文本分类模型尽管引入了标签语义,但利用标签语义来探索文本和标签之间的语义潜在关系仍存在不足。对此,该文将标签合并成序列,并使用链接策略在同一空间内联合学习文本和标签特征来捕获文本和标签的语义。然后,通过注意力机制将标签语义和文档内容相结合生成感知文本,有效地探索文本信息和标签语义的交互关系。此外,该文通过融合机制将粗粒度层次特征和细粒度特征相结合,帮助模型更好地学习不同层次粒度的文档语义信息。在三个公开的数据集Eurlex-4K、Wiki10-30K和Kan-Shan Cup上进行了模型验证,实验结果表明,该文所提模型P@k值优于对比模型,综合性能得到有效提升。

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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: 2025-09-17 14:17:15.023767
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMrAJmx70DCirA2YYwkvdROAdlNdlN2cG1N_7oKb-jbJCGIGagMYtcrdEuvKTlhlvhZzWGHgIPd8-MexbYek7PlMnHWO5PfNZK6XPOypITfZo1fGLLOv4qLwGSNdbJWuwx5GOOnhLeVGS9rgaBX63AHljFgxnlPPfjfMoT8JivfJw==&uniplatform=NZKPT&language=CHS

summary:
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author: 杨泽斌;洪玲儿;胡亦菲;吴先球
date: 2025-06-20
keywords: 中文文本分类;;SOLO分类理论;;物理实验;;教学评价;
source: 物理实验
site: 知网
title: 中文文本分类在物理实验教学评价中的应用——以物理实验课后练习题文字回答的自动评分为例
created_at: 2025-09-17 14:17:15.026044
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNlsrEBH-32lagOyu7m7nkfp95rJUi848aIoj29O-BLA3TTeNAqGyLYks4iNqGiI3zcPwVZDzjaL8jUP_ihPnxI2oCpHdad5BRK2HKWM5T6bO91JOfudLf1bFyD6ciVQku-tGF_8_pLgjqmUOnNeflzYsiilKes7-NR4lIG3mlArA==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u7269\u7406\u5b9e\u9a8c\u8bfe\u7a0b\u4e2d\u7684\u6559\u5b66\u8bc4\u4ef7\u9700\u8981\u5bf9\u5b66\u751f\u8bfe\u540e\u7ec3\u4e60\u9898\u7684\u6587\u5b57\u56de\u7b54\u8fdb\u884c\u5206\u7c7b\u548c\u8bc4\u5206\uff0c\u4f46\u4f20\u7edf\u4eba\u5de5\u8bc4\u4ef7\u65b9\u6cd5\u8017\u65f6\u4e14\u4e3b\u89c2\uff0c\u96be\u4ee5\u6ee1\u8db3\u9ad8\u6548\u3001\u5ba2\u89c2\u7684\u6559\u5b66\u8bc4\u4ef7\u9700\u6c42\u3002", "contribution": "\u8bba\u6587\u5c06\u4e2d\u6587\u6587\u672c\u5206\u7c7b\u6280\u672f\u5e94\u7528\u4e8e\u7269\u7406\u5b9e\u9a8c\u6559\u5b66\u8bc4\u4ef7\uff0c\u5efa\u7acb\u4e86\u57fa\u4e8eSOLO\u7406\u8bba\u7684\u5206\u7c7b\u4f53\u7cfb\uff0c\u63d0\u5347\u4e86\u6559\u5b66\u8bc4\u4ef7\u81ea\u52a8\u5316\u548c\u79d1\u5b66\u5316\u6c34\u5e73\uff0c\u4e3a\u6559\u5e08\u63d0\u4f9b\u4e86\u6539\u8fdb\u6559\u5b66\u7684\u6709\u529b\u5de5\u5177\u3002", "method": "\u4f9d\u636eSOLO\u5206\u7c7b\u7406\u8bba\u5236\u5b9a\u6587\u5b57\u56de\u7b54\u5206\u7c7b\u89c4\u5219\uff0c\u6bd4\u8f83\u5e76\u9009\u62e9\u4e0d\u540c\u4e2d\u6587\u6587\u672c\u5206\u7c7b\u5668\u7684\u6027\u80fd\uff0c\u5b9e\u73b0\u5bf9\u5b66\u751f\u56de\u7b54\u7684\u81ea\u52a8\u5206\u7c7b\u548c\u8bc4\u4ef7\u3002", "objective": "\u63a2\u7d22\u4e2d\u6587\u6587\u672c\u5206\u7c7b\u6280\u672f\u5728\u5927\u5b66\u7269\u7406\u5b9e\u9a8c\u8bfe\u540e\u7ec3\u4e60\u9898\u6587\u5b57\u56de\u7b54\u81ea\u52a8\u8bc4\u5206\u4e0e\u8bc4\u4ef7\u4e2d\u7684\u5b9e\u9645\u5e94\u7528\uff0c\u4ee5\u63d0\u5347\u6559\u5b66\u8bc4\u4ef7\u7684\u6548\u7387\u4e0e\u79d1\u5b66\u6027\u3002", "results": "\u4e2d\u6587\u6587\u672c\u5206\u7c7b\u6280\u672f\u80fd\u591f\u5b9e\u73b0\u7269\u7406\u5b9e\u9a8c\u8bfe\u540e\u7ec3\u4e60\u9898\u5b66\u751f\u56de\u7b54\u7684\u81ea\u52a8\u5316\u5206\u7c7b\u4e0e\u8bc4\u5206\uff0c\u652f\u6301\u6559\u5e08\u9ad8\u6548\u5730\u8fdb\u884c\u5b66\u751f\u8bc4\u4ef7\u548c\u5206\u7c7b\u7edf\u8ba1\u3002"}


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: 2025-09-17 14:17:15.026574
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPj4dUd-LXfvCT6ZX-uizDFYSecR4xJe_1dHyJ5UoxAU01ABhtsnNUwkN1lmz3nIx0TwMTmNOLPRwrS1cOpA0g_gvXfm0O5oDmRW0oJAyt8iJR3Hj5ukPeY8f9iiclIxGPkcKCw8iv2msADrSs6e7GXy0JRx3Uz2DhnsMaqwJmHIQ==&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\u4ea7\u751f\u4e86\u5927\u91cf\u5305\u542b\u60c5\u611f\u548c\u591a\u7c7b\u522b\u4e3b\u9898\u7684\u6587\u672c\u6570\u636e\uff0c\u5982\u4f55\u9ad8\u6548\u3001\u51c6\u786e\u5730\u8fdb\u884c\u6587\u672c\u60c5\u611f\u548c\u4e3b\u9898\u5206\u7c7b\u5728\u793e\u4f1a\u751f\u6d3b\u4e2d\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002\u4f20\u7edf\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u5728\u5904\u7406\u65e5\u76ca\u590d\u6742\u548c\u7ec6\u7c92\u5ea6\u7684\u60c5\u611f\u8868\u8fbe\u53ca\u591a\u6837\u5316\u7684\u6587\u672c\u7c7b\u522b\u65f6\u9762\u4e34\u6311\u6218\u3002", "contribution": "\u672c\u7814\u7a76\u521b\u65b0\u6027\u5730\u7ed3\u5408\u52a8\u6001\u8bed\u4e49\u5efa\u6a21\u3001\u6743\u91cd\u5206\u914d\u53ca\u7279\u5f81\u63d0\u53d6\uff0c\u63d0\u51fa\u5e76\u9a8c\u8bc1\u4e86\u9002\u7528\u4e8e\u590d\u6742\u6587\u672c\u6570\u636e\u7684\u9ad8\u6548\u5206\u7c7b\u65b9\u6cd5\uff0c\u4e3a\u63d0\u5347\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7cfb\u7edf\u7684\u6587\u672c\u60c5\u611f\u4e0e\u4e3b\u9898\u5206\u7c7b\u80fd\u529b\u63d0\u4f9b\u4e86\u65b0\u7684\u89e3\u51b3\u601d\u8def\u3002", "method": "\u672c\u6587\u8bbe\u8ba1\u4e86\u4e09\u79cd\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u5305\u62ec\u57fa\u4e8e\u6743\u91cd\u5206\u914d\u7b56\u7565\u63d0\u5347\u5c40\u90e8\u60c5\u611f\u7279\u5f81\u7684\u53e5\u5b50\u7ea7\u60c5\u611f\u5206\u7c7b\u65b9\u6cd5\uff0c\u5229\u7528\u52a8\u6001\u9884\u8bad\u7ec3\u6a21\u578b\u5bf9\u6587\u672c\u5411\u91cf\u5316\uff0c\u5e76\u7ed3\u5408\u6df1\u5ea6\u5b66\u4e60\u548c\u7279\u5f81\u63d0\u53d6\u6280\u672f\u5b9e\u73b0\u6587\u672c\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u5e76\u7814\u7a76\u51e0\u79cd\u57fa\u4e8e\u52a8\u6001\u8bed\u4e49\u5efa\u6a21\u548c\u7279\u5f81\u63d0\u53d6\u7684\u65b0\u578b\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u4ee5\u63d0\u5347\u6587\u672c\u60c5\u611f\u53ca\u4e3b\u9898\u591a\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u9002\u5e94\u6027\u3002", "results": "\u63d0\u51fa\u7684\u65b9\u6cd5\u5728\u5e94\u5bf9\u590d\u6742\u7ec6\u7c92\u5ea6\u60c5\u611f\u548c\u591a\u7c7b\u522b\u6587\u672c\u5206\u7c7b\u4e2d\u8868\u73b0\u51fa\u66f4\u597d\u7684\u5206\u7c7b\u6027\u80fd\uff0c\u5c24\u5176\u662f\u5728\u53e5\u5b50\u7ea7\u60c5\u611f\u5206\u7c7b\u4efb\u52a1\u4e2d\uff0c\u589e\u5f3a\u578b\u5c40\u90e8\u8bed\u4e49\u7279\u5f81\u7684\u65b9\u6cd5\u63d0\u5347\u4e86\u5206\u7c7b\u7684\u51c6\u786e\u5ea6\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: 2025-09-17 14:17:15.027075
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNqqyY8_N5Epp6WzhhMAzxjuqHbfrKappXRsqyrE6p2mAj4xyxaoNattUMtGuPR-79rXrmo646sIzaxvDcQXYk3feGzHl0GJiYiSRgsTQp3hu8Ysz32ahXySgN7sC5LLN2WH1ULmsfMPJnM0Qi9ayjTUcVtzuMxG3acLVwNWxGvsdcmUFGCAE2N&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\u6838\u5fc3\u4efb\u52a1\uff0c\u65e8\u5728\u901a\u8fc7\u8bed\u4e49\u7406\u89e3\u548c\u4fe1\u606f\u5f52\u7eb3\u63d0\u5347\u7528\u6237\u4fe1\u606f\u7ba1\u7406\u6548\u7387\u3002\u76ee\u524d\u57fa\u4e8e\u56fe\u5377\u79ef\u7f51\u7edc\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u867d\u63d0\u5347\u4e86\u5206\u7c7b\u7cbe\u5ea6\uff0c\u4f46\u5728\u8bed\u4e49\u5229\u7528\u548c\u7279\u5f81\u8868\u8fbe\u65b9\u9762\u4ecd\u6709\u4e0d\u8db3\u3002", "contribution": "\u5f15\u5165\u6fc0\u6d3b\u51fd\u6570\u4f18\u5316\u548c\u591a\u4efb\u52a1\u5b66\u4e60\u7b56\u7565\uff0c\u663e\u8457\u6539\u5584\u4e86\u57fa\u4e8e\u56fe\u5377\u79ef\u7f51\u7edc\u7684\u6587\u672c\u5206\u7c7b\u6548\u679c\uff0c\u62d3\u5c55\u4e86\u8bcd\u8282\u70b9\u5728\u5206\u7c7b\u4e2d\u7684\u4f5c\u7528\uff0c\u4e3a\u6587\u672c\u8bed\u4e49\u5efa\u6a21\u63d0\u4f9b\u66f4\u4f18\u65b9\u6848\u3002", "method": "\u63d0\u51fa\u548c\u7814\u7a76\u4e24\u79cd\u57fa\u4e8e\u56fe\u5377\u79ef\u7f51\u7edc\u7684\u6587\u672c\u5206\u7c7b\u6a21\u578b\uff0c\u5305\u62ec\u6539\u8fdb\u6fc0\u6d3b\u51fd\u6570\u3001\u5f15\u5165\u591a\u4efb\u52a1\u5b66\u4e60\uff08\u5982\u8bcd\u6027\u6807\u6ce8\u548c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u4f5c\u4e3a\u8f85\u52a9\u4efb\u52a1\uff09\uff0c\u4f18\u5316\u8bcd-\u6587\u6863\u56fe\u7ed3\u6784\u4ee5\u66f4\u597d\u5730\u6316\u6398\u6587\u672c\u8bed\u4e49\u3002", "objective": "\u9488\u5bf9\u4f20\u7edf\u56fe\u5377\u79ef\u7f51\u7edc\u65b9\u6cd5\u5728\u6587\u672c\u5206\u7c7b\u4e2d\u5bf9\u8bed\u4e49\u4fe1\u606f\u5229\u7528\u4e0d\u5145\u5206\u548c\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u4e0d\u8db3\u7684\u95ee\u9898\uff0c\u63d0\u51fa\u6539\u8fdb\u65b9\u6cd5\u4ee5\u63d0\u9ad8\u5206\u7c7b\u51c6\u786e\u7387\u3002", "results": "\u6539\u8fdb\u6a21\u578b\u80fd\u591f\u6709\u6548\u7f13\u89e3\u68af\u5ea6\u6d88\u5931\u95ee\u9898\uff0c\u589e\u5f3a\u4fe1\u606f\u4f20\u9012\u548c\u8bed\u4e49\u5229\u7528\uff0c\u4ece\u800c\u63d0\u5347\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u7684\u8868\u73b0\u548c\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: 2025-09-17 14:17:15.027549
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOhuRL8ZMabc_viSJJXrUQcAGQSesD67yuPujxxhKAwe64BKiYHERrLBy2s7laSh-OFfwhzi49d0Kv3mCZyd_TkRX95dsUBgsojpFohV8LrMk1Kb6D6Px9qzLlb5XjNyk-pr5l63ARM3zeg-5joQj2G-TL5ESrciDb5E9rXt2tZ7A==&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\u91cf\u7684\u5feb\u901f\u589e\u957f\u548c\u4f20\u64ad\u6e20\u9053\u7684\u591a\u6837\u5316\uff0c\u5982\u4f55\u9ad8\u6548\u51c6\u786e\u5730\u5bf9\u65b0\u95fb\u6587\u672c\u8fdb\u884c\u5206\u7c7b\uff0c\u6210\u4e3a\u4fe1\u606f\u7ba1\u7406\u3001\u63a8\u8350\u7cfb\u7edf\u548c\u8206\u60c5\u76d1\u6d4b\u7b49\u9886\u57df\u7684\u91cd\u8981\u8bfe\u9898\u3002\u73b0\u6709\u65b9\u6cd5\u5728\u4e0a\u4e0b\u6587\u4fe1\u606f\u63d0\u53d6\u3001\u8bed\u4e49\u7406\u89e3\u548c\u5c42\u6b21\u6807\u7b7e\u5e94\u7528\u7b49\u65b9\u9762\u5b58\u5728\u660e\u663e\u4e0d\u8db3\u3002", "contribution": "\u8bba\u6587\u521b\u65b0\u6027\u5730\u9488\u5bf9\u65b0\u95fb\u6587\u672c\u4e0d\u540c\u5206\u7c7b\u573a\u666f\u6784\u5efa\u7279\u5f81\u878d\u5408\u6a21\u578b\uff0c\u5b9e\u73b0\u4e86\u5168\u5c40\u3001\u65f6\u5e8f\u53ca\u5c40\u90e8\u7279\u5f81\u52a8\u6001\u4ea4\u4e92\uff0c\u5e76\u5c06\u5c42\u6b21\u6807\u7b7e\u8def\u5f84\u4e0e\u6df1\u5ea6\u8bed\u4e49\u878d\u5408\uff0c\u4e3a\u65b0\u95fb\u6587\u672c\u5206\u7c7b\u9886\u57df\u63d0\u4f9b\u4e86\u9ad8\u6548\u4e14\u6613\u4e8e\u63a8\u5e7f\u7684\u65b0\u65b9\u6cd5\u3002", "method": "\u63d0\u51fa\u4e86\u57fa\u4e8e\u591a\u5143\u7279\u5f81\u878d\u5408\u7684\u4e24\u7c7b\u6a21\u578b\uff1a\u5355\u6807\u7b7e\u5206\u7c7b\u91c7\u7528\u6539\u8fdb\u7684BERT-wwm\u5d4c\u5165\u5c42\u3001\u53cc\u5411\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc\u3001\u591a\u5934\u6ce8\u610f\u529b\u4e0e\u91d1\u5b57\u5854\u5377\u79ef\u534f\u540c\u878d\u5408\uff08CBDN\u6a21\u578b\uff09\uff1b\u5c42\u6b21\u5206\u7c7b\u91c7\u7528\u878d\u5408\u5c42\u6b21\u6807\u7b7e\u8def\u5f84\u63d0\u793a\u5d4c\u5165\u7684RoBERTa\u57fa\u7840\u6a21\u578b\uff0c\u5e76\u5f15\u5165\u6ce8\u610f\u529b\u548c\u5bf9\u6bd4\u5b66\u4e60\u673a\u5236\uff08DHFPE\u6a21\u578b\uff09\u3002", "objective": "\u9488\u5bf9\u65b0\u95fb\u6587\u672c\u5355\u6807\u7b7e\u5206\u7c7b\u548c\u5c42\u6b21\u6807\u7b7e\u5206\u7c7b\u4e2d\u5b58\u5728\u7684\u4e0a\u4e0b\u6587\u8bed\u4e49\u63d0\u53d6\u96be\u3001\u6807\u7b7e\u5229\u7528\u4e0d\u5145\u5206\u7b49\u95ee\u9898\uff0c\u63d0\u51fa\u6709\u6548\u63d0\u5347\u5206\u7c7b\u6027\u80fd\u7684\u65b0\u65b9\u6cd5\u3002", "results": "\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0cCBDN\u548cDHFPE\u6a21\u578b\u5728\u65b0\u95fb\u6587\u672c\u7684\u5355\u6807\u7b7e\u548c\u5c42\u6b21\u5206\u7c7b\u4efb\u52a1\u4e2d\u5747\u663e\u8457\u4f18\u4e8e\u73b0\u6709\u4e3b\u6d41\u65b9\u6cd5\uff0c\u63d0\u5347\u4e86\u8bed\u4e49\u7279\u5f81\u63d0\u53d6\u4e0e\u6807\u7b7e\u4ea4\u4e92\u7684\u6548\u7387\u4e0e\u51c6\u786e\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: 2025-09-17 14:17:15.028063
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPJoptoZOk6SibBTDgv6ML7YX3xPJp8sZDodT1yo4x-SBUT4L0lwtijx0_i5qvG9dKq205R8uTbI8RxNFJpH4e9hccYRWwpDMA15PxCaMWrAJv2L-LTSkGC9r_lqGYJ3WBdVhutG7wBZrYWgozFc9SVMNPnPc67J6-k0VRBfc4WgA==&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\u5927\u91cf\u6cd5\u5f8b\u6587\u6863\u548c\u5224\u4f8b\u6587\u672c\uff0c\u800c\u591a\u6807\u7b7e\u6587\u672c\u5206\u7c7b\u4f5c\u4e3a\u5173\u952e\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\uff0c\u5bf9\u63d0\u5347\u6cd5\u5f8b\u6587\u6863\u68c0\u7d22\u6548\u7387\u548c\u6848\u5377\u7ba1\u7406\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002\u4f46\u73b0\u6709\u65b9\u6cd5\u5728\u7279\u5f81\u63d0\u53d6\u3001\u6807\u7b7e-\u6587\u672c\u5173\u8054\u5efa\u6a21\u53ca\u6807\u7b7e\u591a\u91cd\u76f8\u5173\u6027\u5efa\u6a21\u7b49\u65b9\u9762\u4ecd\u5b58\u5728\u4e0d\u8db3\u3002", "contribution": "\u672c\u6587\u9488\u5bf9\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u4efb\u52a1\u63d0\u51fa\u4e86\u591a\u7279\u5f81\u878d\u5408\u548c\u6807\u7b7e\u8bed\u4e49\u5d4c\u5165\u7b49\u521b\u65b0\u65b9\u6cd5\uff0c\u663e\u8457\u6539\u5584\u4e86\u6a21\u578b\u7684\u6807\u7b7e\u9884\u6d4b\u80fd\u529b\u548c\u4e00\u81f4\u6027\uff0c\u4e3a\u53f8\u6cd5\u9886\u57df\u76f8\u5173\u6280\u672f\u5e94\u7528\u548c\u540e\u7eed\u7814\u7a76\u63d0\u4f9b\u4e86\u65b0\u601d\u8def\u548c\u53c2\u8003\u3002", "method": "\u8bbe\u8ba1\u5e76\u63d0\u51fa\u4e24\u79cd\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u6a21\u578b\uff0c\u5f15\u5165\u591a\u7279\u5f81\u878d\u5408\u3001\u6807\u7b7e\u8bed\u4e49\u5d4c\u5165\u4ee5\u53ca\u6539\u8fdb\u7684\u6ce8\u610f\u529b\u673a\u5236\uff0c\u9488\u5bf9\u7279\u5f81\u5229\u7528\u3001\u6807\u7b7e\u76f8\u5173\u6027\u4e0e\u8bed\u4e49\u5efa\u6a21\u7b49\u8584\u5f31\u73af\u8282\u52a0\u4ee5\u4f18\u5316\u3002", "objective": "\u63d0\u51fa\u5e76\u7814\u7a76\u6539\u8fdb\u7684\u6df1\u5ea6\u5b66\u4e60\u591a\u6807\u7b7e\u5206\u7c7b\u65b9\u6cd5\uff0c\u63d0\u5347\u53f8\u6cd5\u6587\u672c\u5728\u7f6a\u540d\u3001\u6cd5\u5f8b\u6761\u6587\u548c\u5211\u671f\u7b49\u6807\u7b7e\u9884\u6d4b\u4e0a\u7684\u51c6\u786e\u6027\u4e0e\u6548\u7387\uff0c\u89e3\u51b3\u73b0\u6709\u6a21\u578b\u5728\u7279\u5f81\u63d0\u53d6\u3001\u5168\u5c40\u4fe1\u606f\u5efa\u6a21\u3001\u6807\u7b7e\u5173\u8054\u5efa\u6a21\u4e0a\u7684\u95ee\u9898\u3002", "results": "\u63d0\u51fa\u7684\u6a21\u578b\u6709\u6548\u63d0\u5347\u4e86\u53f8\u6cd5\u6587\u672c\u591a\u6807\u7b7e\u5206\u7c7b\u4efb\u52a1\u7684\u7cbe\u5ea6\u548c\u6548\u7387\uff0c\u4f18\u5316\u4e86\u4fe1\u606f\u7279\u5f81\u7684\u63d0\u53d6\u4e0e\u5229\u7528\uff0c\u589e\u5f3a\u4e86\u6807\u7b7e\u4e0e\u6587\u672c\u3001\u6807\u7b7e\u95f4\u7684\u8bed\u4e49\u5173\u8054\u5efa\u6a21\u80fd\u529b\uff0c\u6539\u5584\u4e86\u73b0\u6709\u65b9\u6cd5\u7684\u6027\u80fd\u52a3\u52bf\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: 2025-09-17 14:17:15.028544
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPc6EQ7gX8TIju-CZ52mDHNEquNOLUii0GMglEX6QouKvF1C2KvxU_8nmbXNMGD6Fb0Kxr3F04SxsfeQk-UKqTX9NtdHEuxte5nsSg2j5jG8SPwPrUqjrfAAopcxAlxoDYlQuI2_icNr5sYEB46E6VQPlc8WO0KQmxLCnWRDecA3Q==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u6d77\u91cf\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u5728\u4fe1\u606f\u68c0\u7d22\u3001\u8206\u60c5\u5206\u6790\u3001\u81ea\u52a8\u63a8\u8350\u7b49\u9886\u57df\u5e94\u7528\u5e7f\u6cdb\uff0c\u968f\u7740\u4e92\u8054\u7f51\u548c\u793e\u4ea4\u5a92\u4f53\u53d1\u5c55\uff0c\u6587\u672c\u6570\u636e\u5448\u73b0\u9ad8\u7ef4\u3001\u975e\u7ed3\u6784\u5316\u548c\u52a8\u6001\u53d8\u5316\u7b49\u65b0\u6311\u6218\uff0c\u4f20\u7edf\u89c4\u5219\u548c\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u5728\u5e94\u5bf9\u5927\u89c4\u6a21\u9ad8\u6548\u6587\u672c\u5206\u7c7b\u65f6\u5df2\u663e\u4e0d\u8db3\u3002", "contribution": "\u672c\u7814\u7a76\u63d0\u51fa\u4e86\u517c\u987e\u5168\u5c40\u7ed3\u6784\u4e0e\u4e0a\u4e0b\u6587\u5efa\u6a21\u7684\u65b0\u578b\u6df1\u5ea6\u5b66\u4e60\u6587\u672c\u5206\u7c7b\u67b6\u6784\uff0c\u4e3a\u63d0\u5347\u5927\u89c4\u6a21\u6587\u672c\u6570\u636e\u7684\u5206\u7c7b\u6027\u80fd\u63d0\u4f9b\u4e86\u521b\u65b0\u6027\u6280\u672f\u601d\u8def\uff0c\u5e76\u4e3a\u540e\u7eed\u76f8\u5173\u9886\u57df\u7814\u7a76\u63d0\u4f9b\u4e86\u6709\u4ef7\u503c\u7684\u53c2\u8003\u3002", "method": "\u63d0\u51fa\u4e24\u79cd\u6539\u8fdb\u7b56\u7565\uff1a\u4e00\u662f\u9488\u5bf9\u4f20\u7edf\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5c40\u9650\uff0c\u91c7\u7528\u53d8\u5206\u7ed3\u6784\u4e0e\u52a8\u6001\u8fb9\u6743\u91cd\u5b66\u4e60\u6784\u5efa\u6539\u8fdb\u6a21\u578b\uff08VSD-GCN\uff09\uff0c\u4ee5\u66f4\u597d\u9002\u5e94\u5404\u7c7b\u6587\u672c\u7279\u5f81\u53d8\u5316\uff1b\u4e8c\u662f\u5bf9BERT\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u7684\u63a9\u7801\u7b56\u7565\u8fdb\u884c\u521b\u65b0\uff0c\u63d0\u5347\u6a21\u578b\u5bf9\u5168\u5c40\u62d3\u6251\u7ed3\u6784\u548c\u4e0a\u4e0b\u6587\u8bed\u4e49\u7684\u7efc\u5408\u5efa\u6a21\u80fd\u529b\uff0c\u5e76\u5229\u7528\u591a\u5c42\u6b21\u6ce8\u610f\u529b\u673a\u5236\u548c\u7279\u5f81\u878d\u5408\u65b9\u6cd5\u589e\u5f3a\u5206\u7c7b\u6548\u679c\u3002", "objective": "\u65e8\u5728\u7a81\u7834\u73b0\u6709\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\uff08\u5982\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548cBERT\uff09\u5728\u5904\u7406\u5927\u89c4\u6a21\u6587\u672c\u5206\u7c7b\u65f6\u7684\u5c40\u9650\u6027\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u5206\u7c7b\u51c6\u786e\u6027\u548c\u6cdb\u5316\u80fd\u529b\u3002", "results": "\u6539\u8fdb\u540e\u7684\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548cBERT\u6a21\u578b\u5728\u5927\u89c4\u6a21\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u5747\u663e\u8457\u63d0\u5347\u4e86\u5206\u7c7b\u51c6\u786e\u6027\u548c\u6cdb\u5316\u80fd\u529b\uff0c\u6709\u6548\u7f13\u89e3\u4e86\u539f\u6709\u65b9\u6cd5\u5728\u9002\u5e94\u6027\u548c\u8868\u8fbe\u80fd\u529b\u65b9\u9762\u7684\u4e0d\u8db3\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: 2025-09-17 14:17:15.029029
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPc6EQ7gX8TIju-CZ52mDHNEquNOLUii0GMglEX6QouKoMR70hkNjTTSr78o7wSvUlcF5B0z2WlpvWcauRWKE8W-B_rhAFcgBW4pMRNGJYU9p8NNeYRopdKXJYqL04bBsFx78RvtYVso8myzNIV8X4XJkL8cR2fUFoErJVj5ur6FA==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u533b\u7597\u6587\u672c\u5206\u7c7b\u6709\u52a9\u4e8e\u81ea\u52a8\u8bc6\u522b\u548c\u5f52\u7c7b\u5927\u91cf\u533b\u5b66\u6587\u672c\uff0c\u63d0\u5347\u533b\u751f\u68c0\u7d22\u4fe1\u606f\u7684\u6548\u7387\u548c\u8bca\u65ad\u7684\u51c6\u786e\u6027\uff0c\u662f\u667a\u6167\u533b\u7597\u9886\u57df\u7684\u6838\u5fc3\u6280\u672f\uff0c\u4f46\u7531\u4e8e\u533b\u7597\u6587\u672c\u4e13\u4e1a\u6027\u9ad8\u3001\u672f\u8bed\u590d\u6742\uff0c\u5bf9\u5206\u7c7b\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u4e25\u8c28\u6027\u63d0\u51fa\u4e86\u8f83\u9ad8\u8981\u6c42\u3002", "contribution": "\u672c\u7814\u7a76\u521b\u65b0\u6027\u5730\u5c06\u5f02\u6784\u56fe\u548c\u56fe\u6269\u6563\u76f8\u7ed3\u5408\u7528\u4e8e\u533b\u7597\u6587\u672c\u5206\u7c7b\uff0c\u5145\u5206\u6316\u6398\u6587\u672c\u95f4\u7684\u591a\u5c42\u7ed3\u6784\u5173\u7cfb\uff0c\u4e3a\u63d0\u5347\u533b\u7597\u6587\u672c\u81ea\u52a8\u5316\u5904\u7406\u7684\u51c6\u786e\u6027\u548c\u667a\u80fd\u5316\u63d0\u4f9b\u4e86\u65b0\u65b9\u6cd5\u3002", "method": "\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u5f02\u6784\u56fe\u6269\u6563\u7f51\u7edc\u7684\u533b\u7597\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff08MGD\uff09\uff0c\u901a\u8fc7\u6784\u5efa\u8986\u76d6\u5355\u8bcd\u3001\u53e5\u5b50\u53ca\u5176\u76f8\u4e92\u5173\u7cfb\u7684\u6587\u672c\u5f02\u6784\u56fe\uff0c\u7ed3\u5408\u56fe\u6269\u6563\u6280\u672f\u4ee5\u6355\u6349\u957f\u7a0b\u4f9d\u8d56\uff0c\u5e76\u5229\u7528\u5f02\u6784\u56fe\u5377\u79ef\u7f51\u7edc\u4e0e\u591a\u5c42\u611f\u77e5\u5668\u5b8c\u6210\u6587\u672c\u5206\u7c7b\u3002", "objective": "\u89e3\u51b3\u73b0\u6709\u533b\u7597\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u672a\u80fd\u5145\u5206\u5229\u7528\u6587\u672c\u7ed3\u6784\u4fe1\u606f\u548c\u9ad8\u9636\u90bb\u5c45\u8054\u7cfb\u7684\u95ee\u9898\uff0c\u63d0\u9ad8\u533b\u7597\u6587\u672c\u5206\u7c7b\u7684\u51c6\u786e\u6027\u548c\u6548\u679c\u3002", "results": "\u5b9e\u9a8c\u8868\u660e\uff0c\u6784\u5efa\u6587\u672c\u5f02\u6784\u56fe\u548c\u5f15\u5165\u56fe\u6269\u6563\u673a\u5236\u80fd\u591f\u663e\u8457\u63d0\u5347\u533b\u7597\u6587\u672c\u5206\u7c7b\u7684\u6027\u80fd\uff0c\u4e30\u5bcc\u4e86\u7ed3\u6784\u4fe1\u606f\u548c\u8fdc\u7a0b\u4f9d\u8d56\u7684\u8868\u8fbe\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: 2025-09-17 14:17:15.029522
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlPgLahzl9nG4l0C_tCQEG9aYWSHmDhURXsVeS19gpMwYr2afIRZaVReriqbK5nIbYYJBbXM_spGs789l9Z1ataXh8LAQFmg_Ru4uDDD39DhcFQFofnBgu7EcJ8vZg5fSUq0Mrbvyyjgk84p6RkPEf9XVvtXrPqd80cfQ55bxlubXw==&uniplatform=NZKPT&language=CHS

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

parsed_summary:
  {"background": "\u533b\u7597\u4fe1\u606f\u5316\u63a8\u52a8\u4e0b\uff0c\u764c\u75c7\u76f8\u5173\u7684\u533b\u7597\u6587\u672c\u6570\u636e\u6fc0\u589e\uff0c\u5176\u4e13\u4e1a\u6027\u548c\u72ec\u7279\u7ed3\u6784\u5bfc\u81f4\u4f20\u7edf\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\u9762\u4e34\u6311\u6218\uff0c\u4e9f\u9700\u66f4\u9002\u5408\u533b\u5b66\u573a\u666f\u7684\u5904\u7406\u4e0e\u5206\u6790\u65b9\u6cd5\u3002", "contribution": "\u7cfb\u7edf\u5206\u6790\u4e86\u764c\u75c7\u6587\u672c\u5206\u7c7b\u6280\u672f\u5e76\u63d0\u51fa\u521b\u65b0\u6027\u878d\u5408\u65b9\u6cd5\uff0c\u5728\u533b\u5b66\u6587\u672c\u7ed3\u6784\u548c\u8bed\u4e49\u5efa\u6a21\u65b9\u9762\u6709\u6240\u7a81\u7834\uff0c\u4e3a\u533b\u7597\u6587\u672c\u667a\u80fd\u5904\u7406\u63d0\u4f9b\u4e86\u5b9e\u7528\u548c\u521b\u65b0\u7684\u89e3\u51b3\u65b9\u6848\u3002", "method": "\u901a\u8fc7\u6587\u672c\u8868\u793a\u5b66\u4e60\u5206\u522b\u6784\u5efa\u5355\u8bcd\u8282\u70b9\u548c\u53e5\u5b50\u8282\u70b9\u7684\u7279\u5f81\u89c6\u56fe\uff0c\u91c7\u7528\u4e13\u95e8\u7684\u56fe\u5377\u79ef\u6a21\u5757\u5bf9\u4e0d\u540c\u89c6\u56fe\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\uff0c\u518d\u5bf9\u89c6\u56fe\u7279\u5f81\u8fdb\u884c\u878d\u5408\u4ee5\u83b7\u5f97\u5b8c\u6574\u6587\u672c\u8868\u793a\uff0c\u5b9e\u73b0\u764c\u75c7\u6587\u672c\u9ad8\u6548\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u5e76\u9a8c\u8bc1\u4e00\u79cd\u878d\u5408\u591a\u89c6\u56fe\u548c\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u65b0\u578b\u764c\u75c7\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u65e8\u5728\u63d0\u5347\u5206\u7c7b\u51c6\u786e\u6027\u4e0e\u5145\u5206\u6355\u6349\u4e13\u4e1a\u8bed\u4e49\u7279\u5f81\u3002", "results": "\u5b9e\u9a8c\u7ed3\u679c\u663e\u793a\uff0c\u8be5\u591a\u89c6\u56fe\u878d\u5408\u548c\u56fe\u5377\u79ef\u65b9\u6cd5\u5728\u764c\u75c7\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u6574\u4f53\u6027\u80fd\u4f18\u4e8e\u73b0\u6709\u5176\u4ed6\u65b9\u6cd5\uff0c\u6709\u6548\u5904\u7406\u4e86\u4e13\u4e1a\u672f\u8bed\u548c\u7ed3\u6784\u7279\u6b8a\u6027\u7684\u96be\u9898\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: 2025-09-17 14:17:15.030003
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMegQu8PvRsNWOKgGemHkq8V4hf1vFllgs0fF3eeBxTelz5VlTZ-Eb65G0yaiAOAqutS7H6NoFDm1wMC-dgdtuwt3auQw17y2ln0VhEpBylF-mrvpjQ42qgvQDaRPH0JOf-E3zzF-Uy7XynzyllFmfYosE2gJspTmj_iB9z8AUUAZqRPlD04KsQ&uniplatform=NZKPT&language=CHS

summary:
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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: 2025-09-17 14:17:15.030491
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlP2EmFoI8y3Hpr-OWJ5wHFomZcrN6wX0xdnzqxGUvZ0Q8gZcoWuSXfb72PiwsxPa09ZRIZS7fgFSAOXvf6Clig1gFJl0kIhwQxpoeNRdS8GWwmmyfppGyVhztxQIT294R6k2Jki0EgnWMgBkC8GEFUKyprH-jkD5R9iLgYes8bYtg==&uniplatform=NZKPT&language=CHS

summary:
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author: 孙鑫
date: 2025-05-27
keywords: 自然语言处理;;少样本文本分类;;元学习;;对比学习;
source: 黑龙江大学
site: 知网
title: 基于元学习的少样本文本分类方法研究
created_at: 2025-09-17 14:17:15.030966
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlP2EmFoI8y3Hpr-OWJ5wHFomZcrN6wX0xdnzqxGUvZ0Q7WR3XOvD_4FnEUZ0BYJhAqkdioq54tSrYPTiSviN32bPqRGmX0FYkFpDJWNv1q5-QSPSsyLh2DzwRVCXl3rFWgHQZokac-oaHmEC9lB5u04rA2UXeu8Sku0RUIQQrdX5Q==&uniplatform=NZKPT&language=CHS

summary:
  文本分类任务是自然语言处理(NLP)领域的基本问题之一,有着十分广泛的应用场景,如情感分类、虚假新闻检测、实体识别等。在文本分类过程中,模型将输入的样本分配至一个或多个类别中。传统的文本分类方法通常为有监督算法,需要大量的有标签数据对模型进行训练,这在现实的应用场景中往往难以满足。针对这个问题,少样本文本分类任务应运而生。少样本文本分类是指在模型训练过程中,每个类别仅有少数训练样本,通过一定的逻辑将文本分配到一个或多个类别中,有着十分广泛的应用前景。 近年来,元学习方法已经成为解决少样本学习任务的主要方式。虽然现有一些基于元学习的少样本文本分类方法已经取得了良好表现,但仍存在一些待改进的缺点。现有基于元学习的少样本文本分类方法通常聚合样本特征来获取类别原型的嵌入表示,类别原型将随着选取样本的不同而发生变化,具有不稳定性。其次,在少样本分类场景下,模型难以学习到足够的语义信息,进一步导致了模型的性能下降,同时也存在易过拟合的问题。 本文旨在提高少样本场景下文本分类任务性能,提出如下三个方法。 第一,本文提出了基于元学习与外部知识的少样本文本分类算法。本方法使用外部知识提高类别原型的稳定性,...

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author: 周明军
date: 2025-05-24
keywords: 藏文;;文本分类;;预训练语言模型;;提示学习;
source: 西藏大学
site: 知网
title: 基于提示学习的藏文文本分类研究
created_at: 2025-09-17 14:17:15.031432
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlONHIMrHC7wdTPYmhaC2eJVxzYsdWtb-jY_X8gXHw9SISAA0J0E-RYtXxfKnOQE67o0WtJbAJklcq_LKD8VLTDOA4zFfSKbty18OuhTp5tQTyOGuwwCDI2Amh4gc-PKvErXAQN33pzEa8eV1S9fQkbC0c5DG0odKlUWWtCBznDiQA==&uniplatform=NZKPT&language=CHS

summary:
  藏文文本分类作为藏语自然语言处理(Natural Language Processing,NLP)中的关键任务,广泛应用于舆情监测、新闻推送、电子邮件筛选等领域。然而,当前该任务面临两个核心挑战:一是藏文标注数据资源极为有限,二是现有藏文预训练语言模型(Pre-trained Language Model,PLM)在小样本条件下的性能表现尚难令人满意。因此,如何在低资源场景下有效提升藏文文本分类性能成为亟需解决的重要问题。近年来,提示学习(Prompt Learning)作为一种高效的模型微调范式,在少样本学习中表现出显著优势。本文围绕提示学习在藏文文本分类中的应用展开研究,选取跨中国少数民族语言预训练模型CINO(A Chinese Minority Pre-trained Language Model)作为基础模型,设计并验证了多种提示学习微调策略,旨在为藏语自然语言处理任务提供更具实用性的技术路径。主要工作与创新如下: (1)提出符合藏文语言规范的文本预处理方法。针对藏文文本在标点符号使用上的特殊性,本文设计了一种更符合藏文词句构成规律的预处理方法,并构建了一个六分类新闻原始文本数...

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author: 王京
date: 2025-05-01
keywords: 少样本学习;;元学习;;主动学习;;生成对抗网络;;自适应合成抽样方法;
source: 山东财经大学
site: 知网
title: 基于改进元学习算法的少样本文本分类研究
created_at: 2025-09-17 14:17:15.031924
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOzuJ541uJ2cpe9QsObSkYioURT_a6EpXF7ZsbZwC53mxy5aM7rZUtoJ_8xvRkZ_taKkgPLV4K4XQcvfd_s4SfCQJSi_mKvPmuc5J3hXROvhyBz388IvrgsAJfGbyp2lFsG-sJuzH3cHvLY5IJ5_PbGinZ4rpMMv7NMlEUL2-yCFg==&uniplatform=NZKPT&language=CHS

summary:
  在当今信息时代,文本数据的生成速度远远超过了人类处理和理解的能力。所以,如何有效地从未结构化的文本数据中提取出有价值的信息,成为一个亟需解决的问题。目前,传统基于机器学习的文本分类方法依赖于人工制定的文本特征提取规则,在表达文本特征方面较弱,难以捕捉文本间的复杂关系。基于深度学习的方法无需人工提取特征,而是通过深度学习模型对文本进行编码,提高分类准确性。但是,深度学习方法却高度依赖大量标注数据。如何在只有少量标注样本的情况下实现高效准确的文本分类,已然成为了一个重要的研究课题。 因此,本文基于MLADA框架(1)提出图神经驱动的元主动学习模型,解决传统方法过度依赖标注数据以及领域差异导致负迁移使模型性能不佳的问题;(2)提出ADASYN增强的元学习模型,解决模型泛化能力不足以及类别不均衡下性能偏差的问题。具体研究内容与贡献如下: 1.图神经驱动的元主动学习模型GMAL:在主动学习阶段,使用Text GCN采样策略,它整合词袋模型、TF-IDF向量等多类型特征来构建文本图,其中文档-单词边与单词-单词边分别依据TF-IDF和PMI计算权重,以此捕捉语义关系。之后通过图卷积操作更新节点特征,...

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author: 武同心;纪鑫;杨成月;陈屹婷;杨智伟
date: 2025-04-27 09:06
keywords: 深度学习;;电力领域;;文本分类;;卷积神经网络;;注意力机制;
source: 中国电力
site: 知网
title: 基于双层注意力机制的电力文本分类模型
created_at: 2025-09-17 14:17:15.032396
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlNwzGqTEDazErdPZ28dq_azJU60vJGh1GUXXAxC4gy2RCr3IBhARp3kESbIcCQnW7WOm6DGe2tpLHWaIfHA6NHRoHGU1zLkS-l11y0vamBbj9U8RUZ6Vyn3bu-XUajpD4SDiC1uV_5FaFxknCPn7bMXqTePhWCwQG3av0xO8cvCJw==&uniplatform=NZKPT&language=CHS

summary:
  电力领域中存在大量的中文文本数据，传统文本挖掘方法存在分词难度大、文本特征提取具有局限性、处理文本中的复杂关系时效果不好等问题，严重限制了对电力信息的深度理解与分类。基于此，提出了一种结合文本卷积网络(text convolutional neural networks， TextCNN)与注意力(Attention)机制的电力文本分类模型，对输入层、TextCNN层、第一个注意力层、池化层、第二个注意力层以及输出层进行了分层优化设计，并通过实验对模型进行了验证。结果显示，TextCNN-Attention模型在电力文本数据集上的文本分类准确率达到了96.8%，精确率达到了86.3%，召回率达到了90.3%，综合评价值达到了88.2%，表明TextCNN-Attention模型可有效提升电力文本分类的效率，为深度学习算法在电力文本分类领域的应用提供了经验借鉴。

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author: 许惠惠
date: 2025-04-25
keywords: Mega模型;;文本分类;;长文本生成;;门控注意力机制;;指数移动平均;
source: 信息技术与信息化
site: 知网
title: 基于Mega模型的文本分类与长文本生成研究
created_at: 2025-09-17 14:17:15.032869
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlMBVX_P-IvJVA5dGEG6MtgEi8wIOmwfr1v06Fx22u8RGaSiQdrZNV1J0IAITEgow9H-TFNlZJW2maRsHA9JaT-w8GBQSaHJIUj6O3ORnMfumT9LhAAxgfJlZheHKXBOJ2zGNGRDRsaPh1oWrS9zTovxjFpl97_fgE7Dfu5LWUjHPA==&uniplatform=NZKPT&language=CHS

summary:
  随着社交媒体、新闻平台和电商评论等领域中长文本数据的激增，传统模型如 RNN 和 LSTM 在建模长距离依赖方面存在局限，而 Transformer 在处理超长文本时计算开销巨大。为此，文章提出基于 Mega(moving average equipped gated attention)模型的长文本分类与生成方法。通过引入指数移动平均(exponential moving average,EMA)和门控注意力机制，Mega模型增强了长距离依赖建模能力，并通过Mega-chunk 机制提升计算效率。在文本分类任务中，Mega在 AG News、IMDB 和 Amazon Reviews 数据集上的表现优于RNN、LSTM、Tansformer 和Longformer，尤其在长文本分类中展现了更高的准确率和计算效率。综上，Mega 模型通过创新设计，在长文本处理任务中实现了性能与效率的平衡，适用于智能问答、新闻摘要等实际场景。

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author: 赵青
date: 2025-03-25
keywords: 农业乡土文化;;文本分类;;卷积神经网络;;双向长短期记忆;;神经网络;
source: 信息技术
site: 知网
title: 基于CNN-Bi-LSTM的农业乡土文化文本分类模型构建
created_at: 2025-09-17 14:17:15.033360
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=oIgrtk8pnlOZRYv14cmUOSPMHlziw3yG191fPU9wg7jYrs7oV9BNrX3OsXRxAy-xACgctwsqk_d9s14uEDNURJaFKBvQP4JJRUgBqBKk1aAzN9vqB_IIWv_sg4Pwg0qzRfbGLC0Gf1y0Ep5ud_zOhqUaKhwpP3EXxrIzdumdUawi8F8yvR9L6Q==&uniplatform=NZKPT&language=CHS

summary:
  针对当前网络媒体中的农业文本分类提取困难的问题，文中提出一种基于多层卷积神经网络结构(CNN)结合双向长短期记忆网络(Bi-LSTM)的文本分类模型。模型采用Jieba分词工具进行文本预处理，在CNN模型的文本特征提取的基础上，引用LSTM门控机制优化结构。实验表明，运用该模型对农业乡土文化文本进行分类时，数据获取精度均保持在90%左右。实验证明了基于CNN-Bi-LSTM的优化模型在农业乡土文化文本分类识别中具备较高的精度性能和稳定性。

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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: 2025-09-17 14:17:15.033827
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAyGghvZzJ1Y3dwaQ%3D%3D

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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: 2025-09-17 14:17:15.034315
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA0GghvZzJ1Y3dwaQ%3D%3D

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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: 2025-09-17 14:17:15.034797
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDA1GghvZzJ1Y3dwaQ%3D%3D

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

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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: 2025-09-17 14:17:15.035264
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTA0MDEyGghvZzJ1Y3dwaQ%3D%3D

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

parsed_summary:
  {"background": "\u968f\u7740\u6cd5\u5f8b\u9886\u57df\u5bf9\u667a\u80fd\u5316\u548c\u81ea\u52a8\u5316\u5904\u7406\u7684\u9700\u6c42\u4e0d\u65ad\u589e\u52a0\uff0c\u5982\u4f55\u63d0\u5347\u6cd5\u5f8b\u6587\u672c\u7684\u7ec6\u7c92\u5ea6\u5206\u7c7b\u6548\u679c\u6210\u4e3a\u7814\u7a76\u70ed\u70b9\u3002\u7136\u800c\uff0c\u73b0\u6709\u7684\u6df1\u5ea6\u56fe\u795e\u7ecf\u7f51\u7edc\u5728\u5904\u7406\u6587\u672c\u7c7b\u4efb\u52a1\u65f6\u4ecd\u9762\u4e34\u8fc7\u5ea6\u5e73\u6ed1\u548c\u957f\u8ddd\u79bb\u4f9d\u8d56\u5efa\u6a21\u4e0d\u8db3\u7b49\u95ee\u9898\uff0c\u9650\u5236\u4e86\u5206\u7c7b\u6027\u80fd\u7684\u63d0\u5347\u3002", "contribution": "\u672c\u7814\u7a76\u63d0\u51fa\u7684FLGNN\u6a21\u578b\u6709\u6548\u7ed3\u5408\u89e3\u8026\u6280\u672f\u548c\u6ce8\u610f\u529b\u6269\u6563\u673a\u5236\uff0c\u663e\u8457\u63d0\u5347\u4e86\u6cd5\u5f8b\u6587\u672c\u7ec6\u7c92\u5ea6\u5206\u7c7b\u6027\u80fd\uff0c\u4e3a\u6cd5\u5f8b\u9886\u57df\u7684\u667a\u80fd\u7ba1\u7406\u548c\u4eba\u5de5\u667a\u80fd\u5e94\u7528\u5e26\u6765\u65b0\u7684\u6280\u672f\u652f\u6491\u3002", "method": "\u63d0\u51fa\u57fa\u4e8e\u6df1\u5ea6\u6ce8\u610f\u529b\u6269\u6563\u56fe\u795e\u7ecf\u7f51\u7edc\uff08FLGNN\uff09\u7684\u65b9\u6cd5\uff0c\u5229\u7528BERT\u9884\u8bad\u7ec3\u6a21\u578b\u83b7\u53d6\u6587\u672c\u8bed\u4e49\u7279\u5f81\uff0c\u5c06\u6587\u672c\u6784\u5efa\u4e3a\u6709\u5411\u56fe\uff0c\u5e94\u7528\u6df1\u5ea6\u89e3\u8026\u56fe\u795e\u7ecf\u7f51\u7edc\u63d0\u53d6\u5168\u5c40\u548c\u9690\u85cf\u7279\u5f81\uff0c\u5e76\u901a\u8fc7\u8282\u70b9\u7ea7\u6ce8\u610f\u529b\u673a\u5236\u4e0e\u7279\u5f81\u878d\u5408\u5b9e\u73b0\u6587\u672c\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u80fd\u591f\u7f13\u89e3\u8fc7\u5ea6\u5e73\u6ed1\u3001\u5f3a\u5316\u957f\u8ddd\u79bb\u4f9d\u8d56\u5efa\u6a21\u3001\u63d0\u5347\u6cd5\u5f8b\u6587\u672c\u7ec6\u7c92\u5ea6\u5206\u7c7b\u51c6\u786e\u7387\u7684\u65b0\u6a21\u578b\u3002", "results": "FLGNN\u6a21\u578b\u5728PKULawData\u6570\u636e\u96c6\u4e0a\u5206\u7c7b\u51c6\u786e\u7387\u9ad8\u8fbe94.85%\uff0c\u5728JSCLawData\u6570\u636e\u96c6\u4e0a\u4e3a90.91%\uff0c\u5747\u663e\u8457\u4f18\u4e8eBERT\u3001DADGNN\u548cRCNN\u7b49\u4e3b\u6d41\u57fa\u7ebf\u6a21\u578b\uff0c\u663e\u793a\u51fa\u5728\u6cd5\u5f8b\u6587\u672c\u7ec6\u7c92\u5ea6\u5206\u7c7b\u4efb\u52a1\u4e0a\u7684\u6709\u6548\u6027\u3002"}


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: 2025-09-17 14:17:15.035754
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD2JqeWRkeDIwMjUwMjAwMhoIb2cydWN3cGk%3D

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

parsed_summary:
  {"background": "\u5728\u6570\u636e\u8981\u7d20\u65f6\u4ee3\uff0c\u79d1\u5b66\u6570\u636e\u96c6\u5bf9\u51b3\u7b56\u7684\u5f71\u54cd\u529b\u88ab\u89c6\u4e3a\u8861\u91cf\u6570\u636e\u96c6\u8d28\u91cf\u4e0e\u4ef7\u503c\u7684\u91cd\u8981\u65b9\u9762\uff0c\u800c\u81ea\u52a8\u5316\u3001\u5ba2\u89c2\u5730\u4ece\u653f\u5e9c\u516c\u6587\u8fd9\u7c7b\u957f\u6587\u672c\u4e2d\u8bc4\u4ef7\u5176\u51b3\u7b56\u5f71\u54cd\u529b\u6210\u4e3a\u65b9\u6cd5\u53d1\u5c55\u7684\u9700\u6c42\u3002", "contribution": "\u521b\u65b0\u6027\u5730\u878d\u5408\u8bcd\u6027\u7279\u5f81\u7528\u4e8e\u957f\u6587\u672c\u5206\u7c7b\uff0c\u63d0\u51fa\u4e86\u95e8\u63a7\u673a\u5236\u4ee5\u4f18\u5316\u4fe1\u606f\u805a\u5408\uff0c\u4e3a\u79d1\u5b66\u6570\u636e\u96c6\u51b3\u7b56\u5f71\u54cd\u529b\u7684\u81ea\u52a8\u5316\u8bc4\u4ef7\u63d0\u4f9b\u4e86\u65b0\u578b\u3001\u6709\u6548\u7684\u6280\u672f\u65b9\u6848\u3002", "method": "\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u8bcd\u6027\u7279\u5f81\u878d\u5408\u7684\u957f\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff08LTC-WF\uff09\uff0c\u901a\u8fc7\u8bcd\u6027\u5206\u7c7b\u3001\u77ed\u8bed\u6574\u5408\u4e0e\u5d4c\u5165\u3001\u4ee5\u53ca\u62fc\u63a5\u878d\u5408\u5355\u5143\u548c\u95e8\u63a7\u878d\u5408\u5355\u5143\u5bf9\u5d4c\u5165\u4fe1\u606f\u8fdb\u884c\u805a\u5408\uff0c\u5b9e\u73b0\u6587\u672c\u7684\u6700\u7ec8\u5206\u7c7b\u3002", "objective": "\u63d0\u51fa\u5e76\u9a8c\u8bc1\u4e00\u79cd\u80fd\u591f\u9ad8\u6548\u3001\u51c6\u786e\u5730\u4ece\u653f\u5e9c\u516c\u544a\u7b49\u957f\u6587\u672c\u4e2d\u5224\u65ad\u4e0e\u79d1\u5b66\u6570\u636e\u96c6\u76f8\u5173\u6027\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u4ee5\u8f85\u52a9\u79d1\u5b66\u6570\u636e\u96c6\u51b3\u7b56\u5f71\u54cd\u529b\u7684\u81ea\u52a8\u8bc4\u4ef7\u3002", "results": "\u5728\u79d1\u5b66\u6570\u636e\u653f\u7b56\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u8868\u660e\uff0c\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\u5728\u51b3\u7b56\u5f71\u54cd\u529b\u8bc4\u4ef7\u4efb\u52a1\u4e2d\u4f18\u4e8e\u73b0\u6709\u4e3b\u6d41\u957f\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff0c\u6027\u80fd\u63d0\u5347\u660e\u663e\u3002"}


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: 2025-09-17 14:17:15.036226
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDXJqeGIyMDI1MDcwMTUaCG9nMnVjd3Bp

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

parsed_summary:
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author: 李倩;赵彦云;燕雪倩
date: 2025年3期
keywords: 新能源;产业政策;实施效果;文本分类;面板分位数回归模型
quote: 0
source: 《干旱区资源与环境》
site: 万方
title: 新能源产业政策文本分类及实施效果差异性研究
created_at: 2025-09-17 14:17:15.036701
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWdocXp5eWhqMjAyNTAzMDAxGghvZzJ1Y3dwaQ%3D%3D

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

parsed_summary:
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author: 谌文佳;杨琳;李金林
date: 2025年2期
keywords: 意图识别;医疗问答;语义相似度分类;深度学习
quote: 1
source: 《数据分析与知识发现》
site: 万方
title: 嵌入意图识别的医疗健康问答文本语义分类模型
created_at: 2025-09-17 14:17:15.037187
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGghvZzJ1Y3dwaQ%3D%3D

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

parsed_summary:
  {"background": "\u5728\u4e2d\u6587\u533b\u7597\u5065\u5eb7\u95ee\u7b54\u573a\u666f\u4e0b\uff0c\u7528\u6237\u67e5\u8be2\u7684\u590d\u6742\u6027\u548c\u591a\u6837\u6027\u5bfc\u81f4\u8bed\u4e49\u7406\u89e3\u548c\u7b54\u6848\u51c6\u786e\u5339\u914d\u9762\u4e34\u6781\u5927\u6311\u6218\uff0c\u73b0\u6709\u6a21\u578b\u5728\u610f\u56fe\u8bc6\u522b\u548c\u8bed\u4e49\u5206\u7c7b\u7684\u51c6\u786e\u6027\u65b9\u9762\u4ecd\u6709\u63d0\u5347\u7a7a\u95f4\u3002", "contribution": "\u672c\u7814\u7a76\u5c06\u610f\u56fe\u8bc6\u522b\u4e0e\u8bed\u4e49\u5206\u7c7b\u6a21\u578b\u878d\u5408\uff0c\u63d0\u5347\u4e86\u533b\u7597\u5065\u5eb7\u95ee\u7b54\u7cfb\u7edf\u7684\u8bed\u4e49\u7406\u89e3\u548c\u5339\u914d\u80fd\u529b\uff0c\u4e3a\u5b9e\u73b0\u66f4\u667a\u80fd\u7cbe\u51c6\u7684\u4e2d\u6587\u533b\u7597\u81ea\u52a8\u95ee\u7b54\u63d0\u4f9b\u4e86\u65b0\u7684\u65b9\u6cd5\u548c\u7406\u8bba\u652f\u6301\u3002", "method": "\u91c7\u7528\u878d\u5408BERT\u548c\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u7684\u65b9\u5f0f\uff0c\u9996\u5148\u8fdb\u884c\u610f\u56fe\u8bc6\u522b\uff0c\u5e76\u5c06\u610f\u56fe\u8bc6\u522b\u6a21\u5757\u4f5c\u4e3a\u5d4c\u5165\u5c42\u6784\u5efa\u5d4c\u5165\u5f0f\u610f\u56fe\u8bc6\u522b\u5b6a\u751fBERT\uff08ITBERT\uff09\u8bed\u4e49\u5206\u7c7b\u6a21\u578b\u3002", "objective": "\u63d0\u51fa\u4e00\u79cd\u57fa\u4e8e\u610f\u56fe\u8bc6\u522b\u7684\u6a21\u578b\uff0c\u4ee5\u63d0\u5347\u533b\u7597\u5065\u5eb7\u95ee\u7b54\u4e2d\u8bed\u4e49\u76f8\u4f3c\u5ea6\u5206\u7c7b\u7684\u51c6\u786e\u6027\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u7cbe\u51c6\u7684\u7b54\u6848\u5339\u914d\u3002", "results": "\u5728CHIP-STS\u6570\u636e\u96c6\u4e0a\uff0c\u878d\u5408\u6a21\u578b\u5728\u610f\u56fe\u8bc6\u522b\u548c\u8bed\u4e49\u76f8\u4f3c\u5ea6\u5206\u7c7b\u4efb\u52a1\u5747\u8868\u73b0\u4f18\u8d8a\uff0cTop-1\u548cTop-3\u610f\u56fe\u8bc6\u522b\u51c6\u786e\u7387\u8f83\u5355\u4e00\u6a21\u578b\u663e\u8457\u63d0\u5347\uff0cITBERT\u6a21\u578b\u7684AUC\u503c\u4e5f\u8f83\u57fa\u51c6\u6a21\u578b\u6709\u660e\u663e\u63d0\u9ad8\uff0c\u9a8c\u8bc1\u4e86\u5d4c\u5165\u610f\u56fe\u77e5\u8bc6\u7684\u6709\u6548\u6027\u3002"}


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: 2025-09-17 14:17:15.037657
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISFWpsZ3lkeHpya3h4YjIwMjUwNTAxORoIb2cydWN3cGk%3D

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

parsed_summary:
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