Biomedical Text Classification Method Based on Hypergraph Attention Network

被引：0
|
作者：
Simeng B. [1 ]
Zhendong N. [1 ]
Hui H. [2 ]
Kaize S. [1 ,3 ]
Kun Y. [1 ]
Yuanchi M. [1 ]
机构：
[1] School of Computer Science & Technology, Beijing Institute of Technology, Beijing
[2] School of Medical Technology, Beijing Institute of Technology, Beijing
[3] Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 11期
关键词：
Biomedical Field Label Information Fusion; Cross Attention Mechanism; Text Classification; Text-Level Hypergraph;
D O I：
10.11925/infotech.2096-3467.2022.0145
中图分类号：
学科分类号：
摘要：
[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. © 2022, Chinese Academy of Sciences. All rights reserved.
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页码：13 / 24
页数：11
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