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现代图书情报技术  2016, Vol. 32 Issue (10): 91-97     https://doi.org/10.11925/infotech.1003-3513.2016.10.10
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结合多种特征的CRF模型用于化学物质-疾病命名实体识别
隋明爽,崔雷()
中国医科大学医学信息学院 沈阳 110122
Extracting Chemical and Disease Named Entities with Multiple-Feature CRF Model
Sui Mingshuang,Cui Lei()
School of Medical Informatics, China Medical University, Shenyang 110122, China
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摘要 

目的】建立结合多种特征的条件随机场模型, 探索从大型生物医学文本中同时自动提取化学物质和疾病实体的方法。【方法】结合命名实体识别特征, 包括词法特征、领域知识特征、词典匹配特征和无监督学习特征等, 比较不同特征对命名实体识别的效果, 并优化模型。【结果】CRF模型纳入词法特征、词典匹配特征、无监督学习特征和部分领域知识特征, 化学物质识别准确率97.33%、召回率80.76%、F值88.27%, 疾病实体识别准确率为84.20%、召回率为81.96%、F值为83.07%。【局限】同时识别化学物质和疾病实体可能存在互相干扰, 删除的部分领域特征可能含有有用信息。【结论】本研究可为生物医学命名实体识别的特征选择提供参考, 同时仍需优化特征以获得更好的识别效果。

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隋明爽
崔雷
关键词 命名实体识别条件随机场文本挖掘无监督学习    
Abstract

[Objective] This study aims to build a CRF model with multiple features, which could automatically extract chemical and disease named entities from biomedical documents. [Methods] We compared the performance of popular named entity recognition features, including lexical features, domain knowledge features, dictionary matching features as well as unsupervised learning features, and then optimized the new model. [Results] We built the final CRF model with lexical features, dictionary matching features, unsupervised learning features and part of the domain knowledge features. The precision, recall, and F-score for chemical entities identification tasks were 97.33%, 80.76%, and 88.27, respectively. For disease entities, they were 84.20%, 81.96%, and 83.07%, respectively. [Limitations] Chemical and disease entities may interfere with each other while being identified simultaneously. The deleted domain knowledge features may contain valuable information. [Conclusions] This study proposed a new method to identify biomedical named entities, which could be further improved.

Key wordsNamed entity recognition    CRF    Text mining    Unsupervised learning
收稿日期: 2016-06-24      出版日期: 2016-11-23
引用本文:   
隋明爽,崔雷. 结合多种特征的CRF模型用于化学物质-疾病命名实体识别[J]. 现代图书情报技术, 2016, 32(10): 91-97.
Sui Mingshuang,Cui Lei. Extracting Chemical and Disease Named Entities with Multiple-Feature CRF Model. New Technology of Library and Information Service, 2016, 32(10): 91-97.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2016.10.10      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2016/V32/I10/91
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