Normalizing Chinese Disease Names with Multi-feature Fusion
Han Pu1,2(),Zhang Zhanpeng1,Zhang Mingtao1,Gu Liang1
1School of Management, Nanjing University of Posts & Telecommunications, Nanjing 210023, China; 2Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This paper proposes a normalization model for Chinese disease names based on multi-feature fusion, aiming to address the issue of multiple alternative disease names for online health communities. [Methods] First, we constructed a normalized dataset for Chinese disease names used by online health communities. Second, we conducted experiments in Chinese and English with the LSTM, GRU and CNN models. Third, we generated external semantic feature vectors with Word2vec and GloVe. Finally, we developed the normalization model MFCF-CNN for Chinese disease names based on the multi-feature fusion and self-attention mechanism. [Results] We examined the proposed model with dataset. The accuracy of our MFCF-CNN model reached 85.48%, which is 8.84% higher than the basic CNN model. Our model made better use of global and local semantic features. [Limitations] The amount of the experiment data needs to be expanded. [Conclusions] The proposed model promotes the normalization of Chinese disease names, which benefits the medical knowledge graph construction and natural language understanding in Chinese.
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