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Study on the Method of Causality Extraction from Chinese Medical Texts by Integrating Relational Label and Location Information / 医学信息学杂志
Article ي Zh | WPRIM | ID: wpr-1023469
المكتبة المسؤولة: WPRO
ABSTRACT
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.
Key words
النص الكامل: 1 الفهرس: WPRIM اللغة: Zh مجلة: Journal of Medical Informatics السنة: 2024 نوع: Article
النص الكامل: 1 الفهرس: WPRIM اللغة: Zh مجلة: Journal of Medical Informatics السنة: 2024 نوع: Article