吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 1843-1848.doi: 10.13229/j.cnki.jdxbgxb201406047

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Named entity recognition in Chinese medical records based on cascaded conditional random field

YAN Yang1, 2, WEN Dun-wei3, WANG Yun-ji1, WANG Ke1   

  1. 1.College of Communication Engineering, Jilin University, Changchun 130012, China;
    2.College of Computer Science and Engineering, Changchun Normal University of Technology, Changchun 130032, China;
    3.School of Computing and Information Systems, Athabasca University, Athabasca, Alberta T9S3A3, Canada
  • Received:2013-08-12 Online:2014-11-01 Published:2014-11-01

Abstract: A new method for named entity recognition in Chinese medical records based on cascaded Conditional Random Fields (CRFs) is proposed. The first layer of the cascaded CRFs is used to identify the basic named entities of body parts and diseases. Then, the identified results are fed to the second layer for recognition of nested named entities for complex diseases and clinical symptoms. A new combination feature, composed of part-of-speech features and named entity features, is defined. This new feature together with the character features, word boundary features and context features in a sentence are taken as the feature set of the second layer. In the experiments based on CRF++, the proposed method yields a 3% higher F-score than cascaded CRF without the combination feature. Moreover, compared to single layer CRF method, it yields a 7% higher F-score, a significant increase in overall performance.

Key words: information processing, conditional random field, cascaded conditional random field, Chinese medical records, named entity recognition

CLC Number: 

  • TP391
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