Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition

Named Entity Recognition (NER) is a natural language processing task for recognizing named entities in a given sentence. Chinese NER is difficult due to the lack of delimited spaces and conventional features for determining named entity boundaries and categories. This study proposes the ME-MGNN (Mul...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2021-07, Vol.25 (7), p.2801-2810
Main Authors: Lee, Lung-Hao, Lu, Yi
Format: Article
Language:English
Subjects:
Adaptation models
Annotations
Diseases
Embedding representation
Graph neural networks
Health care
Hidden Markov models
information extraction
Medical services
named entity recognition
Natural language processing
Neural networks
Recognition
Sentences
Task analysis
Training
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Summary:Named Entity Recognition (NER) is a natural language processing task for recognizing named entities in a given sentence. Chinese NER is difficult due to the lack of delimited spaces and conventional features for determining named entity boundaries and categories. This study proposes the ME-MGNN (Multiple Embeddings enhanced Multi-Graph Neural Networks) model for Chinese NER in the healthcare domain. We integrate multiple embeddings at different granularities from the radical, character to word levels for an extended character representation, and this is fed into multiple gated graph sequence neural networks to identify named entities and classify their types. The experimental datasets were collected from health-related news, digital health magazines and medical question/answer forums. Manual annotation was conducted for a total of 68,460 named entities across 10 entity types (body, symptom, instrument, examination, chemical, disease, drug, supplement, treatment and time) in 30,692 sentences. Experimental results indicated our ME-MGNN model achieved an F1-score result of 75.69, outperforming previous methods. In practice, a series of model analysis implied that our method is effective and efficient for Chinese healthcare NER.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2020.3048700