摘要
【目的】融合标签语义信息,采用文本级超图和交叉注意力机制捕捉文献文本的组织结构及语义语法信息,提高生物医学领域的文本分类效果。【方法】使用经微调的BioBERT模型从生物医学领域文本中获取向量特征,构建文本级超图捕获文本的语序、语义及语法信息,通过提出的交叉注意力机制网络将文本级超图和标签语义信息进行特征融合实现文本分类任务。【结果】在数据集PM-Sentence数据集上的实验结果表明,所提模型相较于基线模型在综合评价F1指标上最大提高2.34个百分点。【局限】构建的数据集有待扩充,对所提模型用于该领域其他任务的适用性有待进一步研究。【结论】所提模型提升了生物医学文本的分类效果,为知识检索、知识挖掘等知识服务应用提供了有效支持。
[Objective]This paper proposes a new model integrating tag semantics.It uses text-level hypergraph and cross attention mechanism to capture the organizational structure and grammatical semantics of literature,aiming to improve the classification of biomedical texts.[Methods]First,we utilized the fine-tuned BioBERT to retrieve vector features from the biomedical texts.Then,we constructed a text-level hypergraph to capture the word order,semantics,and syntactics of the texts.Finally,we merged the features of text-level hypergraph and labelled semantics through the cross attention mechanism network to finish the text classification.[Results]The experimental results on the PM-Sentence dataset show that the proposed model is 2.34 percentage points higher than the baseline model in the comprehensive evaluation of F1 indicators.[Limitations]The experimental dataset needs to be expanded to evaluate the model’s performance in other fields.[Conclusions]The newly constructed model improves the classification of biomedical texts and provides effective support for knowledge retrieval and mining.
作者
白思萌
牛振东
何慧
时恺泽
易坤
马原驰
Bai Simeng;Niu Zhendong;He Hui;Shi Kaize;Yi Kun;Ma Yuanchi(School of Computer,Beijing Institute of Technology,Beijing 100081,China;School of Medical Technology,Beijing Institute of Technology,Beijing 100081,China;Australian Artificial Intelligence Institute,University of Technology Sydney,Sydney 2007,Australia)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2022年第11期13-24,共12页
Data Analysis and Knowledge Discovery
基金
国家重点研发计划(项目编号:2019YFB1406303)的研究成果之一。
关键词
文本分类
文本级超图
交叉注意力机制
生物医学领域
标签信息融合
Text Classification
Text-Level Hypergraph
Cross Attention Mechanism
Biomedical Field
Label Information Fusion