基于超图注意力网络的生物医学文本分类方法

Biomedical Text Classification Method Based on Hypergraph Attention Network

【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.