Incorporating Domain Knowledge into Natural Language Inference on Clinical Texts

IEEE Access

Yu Fang¹  Mingming Lu¹  Fengqi Yan¹  Maozhen Li² 

[1] Department of Computer Science and Technology, Tongji University, Shanghai, China;Department of Electronic and Computer Engineering, Brunel University London, UB8 3PH, Uxbridge, U.K.;

关键词: Attention mechanism;    clinical text;    medical domain knowledge;    natural language inference;    word representation;   

DOI  :  10.1109/ACCESS.2019.2913694

来源: DOAJ

【 摘 要 】

Making inference on clinical texts is a task which has not been fully studied. With the newly released, expert annotated MedNLI dataset, this task is being boosted. Compared with open domain data, clinical texts present unique linguistic phenomena, e.g., a large number of medical terms and abbreviations, different written forms for the same medical concept, which make inference much harder. Incorporating domain-specific knowledge is a way to eliminate this problem, in this paper, we assemble a new incorporating medical concept definitions module on the classic enhanced sequential inference model (ESIM), which first extracts the most relevant medical concept for each word, if it exists, then encodes the definition of this medical concept with a bidirectional long short-term network (BiLSTM) to obtain domain-specific definition representations, and attends these definition representations over vanilla word embeddings. The empirical evaluations are conducted to demonstrate that our model improves the prediction performance and achieves a high level of accuracy on the MedNLI dataset. Specifically, the knowledge enhanced word representations contribute significantly to entailment class.

【 授权许可】

Unknown