Biomedical Text Classification Method Based on Hypergraph Attention Network
- Publication Type:
- Journal Article
- Citation:
- 2022, 6, (11), pp. 13-24
- Issue Date:
- 2022-11-25
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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 2022 Chinese Academy of Sciences All rights reserved
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