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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (11): 13-24    DOI: 10.11925/infotech.2096-3467.2022.0145
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Biomedical Text Classification Method Based on Hypergraph Attention Network
Bai Simeng1,Niu Zhendong1(),He Hui2,Shi Kaize1,3,Yi Kun1,Ma Yuanchi1
1School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
2School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
3Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney 2007, Australia
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[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.

Key wordsText Classification      Text-Level Hypergraph      Cross Attention Mechanism      Biomedical Field      Label Information Fusion     
Received: 23 February 2022      Published: 13 January 2023
ZTFLH:  TP391  
Fund:National Key R&D Program of China(2019YFB1406303)
Corresponding Authors: Niu Zhendong     E-mail:

Cite this article:

Bai Simeng,Niu Zhendong,He Hui,Shi Kaize,Yi Kun,Ma Yuanchi. Biomedical Text Classification Method Based on Hypergraph Attention Network. Data Analysis and Knowledge Discovery, 2022, 6(11): 13-24.

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Label-Based Bio-Hypergraph Attention Mechanism Network
Dependency Structure Diagram
主题词 数量 抽取数量
解剖学(Anatomy) 6 466 14
有机体(Organisms) 2 801 6
疾病(Diseases) 8 421 18
化学品和药物(Chemicals and Drugs) 1 862 4
(Analytical, Diagnostic and Therapeutic
Techniques and Equipment)
513 1
精神病学和心理学(Psychiatry and Psychology) 1 537 3
现象和过程(Phenomena and Processes) 511 1
学科和职业(Disciplines and Occupations) 459 1
(Anthropology, Education, Sociology and
Social Phenomena)
747 2
技术、工业、农业(Technology, Industry,
2 305 5
人文(Humanities) 1 743 4
信息科学(Information Science) 4 263 9
命名组(Named Groups) 264 1
医疗保健(Health Care) 3 483 8
出版特征(Publication Characteristics) 409 1
地理(Geographicals) 744 2
总计 36 528 80
MeSH Statistics of PubMed Scientific and Technological Literature
分类标签 数量
背景(Background) 1 638
目的(Objective) 1 400
方法(Methods) 5 815
结果(Results) 6 095
结论(Conclusions) 2 670
总计 17 618
Label Statistical Analysis of PM-Sentence Dataset
模型 P/% R/% F1/% Acc/%
SVM+Text Features 59.97 61.58 60.76 59.86
LSTM 60.55 62.87 61.69 60.54
CNN 62.25 63.74 62.99 59.04
HAN 67.94 67.98 67.96 63.69
TextGCN 69.90 70.35 70.12 69.97
Text-Level GNN 70.12 71.05 70.58 70.51
HyperGAT 72.52 71.58 70.86 71.85
LBGAT(本文) 73.62 73.04 73.20 73.04
The Experimental Results of Test Samples of Different Models
模型 P/% R/% F1/% Acc/%
w/o sequential 66.51 65.98 66.24% 66.93
w/o semantic 69.34 68.29 68.81 69.57
w/o syntactic 68.66 67.95 68.30 68.36
w/o BDE 65.21 64.57 64.89 65.57
w/o TLCA 63.24 61.29 62.25 62.77
LBGAT(本文) 73.62 73.04 73.20 73.04
The Experimental Results of Test Samples of LBGAT and Five Variants
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