【摘 要】
：
Named entity recognition(NER)in Chinese electronic medical records(EMRs)has become an important task of clinical natural language processing(NLP).However,limited studies have been performed on the cli
【机 构】
：
College of Computer Science and Engineering,Northwest Normal University,China
【出 处】
：
第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)
论文部分内容阅读
Named entity recognition(NER)in Chinese electronic medical records(EMRs)has become an important task of clinical natural language processing(NLP).However,limited studies have been performed on the clinical NER study in Chinese EMRs.Furthermore,when end-to-end neural network models have improved clinical NER performance,medical knowledge dictionaries such as various disease association dictionaries,which provide rich information of medical entities and relations among them,are rarely utilized in NER model.In this study,we investigate the problem of NER in Chinese EMRs and propose a clinical neural network NER model enhanced with medical knowledge attention by combining the entity mention information contained in external medical knowledge bases with EMR context together.Experimental results on the manually labeled dataset demonstrated that the proposed method can achieve better performance than the previous methods in most cases.
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