Biomedical Text Classification Method Based on Hypergraph Attention Network

[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.