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2018 | OriginalPaper | Buchkapitel

Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR

verfasst von : Zhichang Zhang, Yu Zhang, Tong Zhou

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

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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Titel: Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR
verfasst von: Zhichang Zhang
Yu Zhang
Tong Zhou
Verlag: Springer International Publishing
Buch: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01716-3_31

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