Abstract: With the rapid advancement of China’s biomedical industry,the volume of Chinese medical texts is escalating at a rapid pace.Extracting valuable information from these texts can ease the learning curve for practitioners.To tackle the challenge of entity relation extraction in the realm of Chinese medicine,a series of models based on bidirectional LSTM have been previously proposed.However,to overcome the training speed bottleneck inherent to bidirectional LSTM,this study introduces the Cascade binary pointer network framework to the domain of Chinese medical filed.To address the framework’s weak capability in identifying main entities and the gradient issues arising from reusing the coding layer,this paper introduces the main entity enhancement module and employs conditional layer normalization.This paper presents the subject enhanced cascade binary pointer tagging framework for chinese medical text (SE-CAS),tailored for Chinese medical text.The subject enhancement module accurately identifies valid subjects detected by the subject recognition module and rectifies erroneously identified entities.Furthermore,the conditional layer normalization method replaces the simplistic addition between word embeddings and subject embeddings found in the original model.Experimental results demonstrate that the proposed model achieves a 5.73% enhancement in F1 measure on the CMeIE dataset.The ablation study confirms the incremental impact of each module,and these improvements exhibit a cumulative effect.

Key words: Entity relation extraction, CASREL, Medical relation extraction, Deeplearning, Subject recognition