Study on the Method of Causality Extraction from Chinese Medical Texts by Integrating Relational Label and Location Information
Purpose/Significance The relative positions of causality words are utilized to assist deep learning models to improve cau-sality prediction and mine medical text gain information.Method/Process The relative position information of causality words in medical texts is represented as a relational feature layer embedded in a pre-trained language model,and the baseline model is integrated for enti-ty recognition and relationship extraction.Result/Conclusion The F1 value of the model embedded in the relational feature layer is im-proved by 2.92 percentage points and 6.41 percentage points compared with the baseline models BERT-BiLSTM-CRF and CasRel,re-spectively,with better causal prediction capacity.
natural language processingcausality extractionpre-training modelBERTmedical text
万方数据
维普
