Abstract
In the massive medical texts, the accuracy of causal relationship extraction is relatively low because of its special characteristic, a high correlation between semantics and radicals. To improve the extraction accuracy, this paper proposes a radical-aware causal relationship extraction model, which is oriented to the medical field. The BERT pre-training model is used to extract character-level features, which contain rich context information. To further deeply capture the semantics of characters, the Word2Vec model is utilised to extract radical features. Finally, the above two features are concatenated and passed into the extraction model to obtain the extraction results. Experimental results show that the proposed model can improve the accuracy of causal relationship extraction in medical texts.
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