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Author:

Gao, Jiangfan (Gao, Jiangfan.) | Chen, Jianhui (Chen, Jianhui.) | Zhang, Shun (Zhang, Shun.) | He, Xiaobo (He, Xiaobo.) | Lin, Shaofu (Lin, Shaofu.)

Indexed by:

CPCI-S

Abstract:

Named entity recognition is a basic and core task of biomedical text mining. Comparing with other named entity recognition methods, methods based on domain relevance measurement need the smaller training corpora and entity samples and are appropriate for recognizing narrow-domain entities, which belong to a subdivision and small semantic class. However, how to obtain the high-quality target corpus set become a key issue. This paper propose a biomedicine named entity recognition method by integrating domain contextual relevance measurement and active learning. Firstly, binding with density-based clustering and semantic distance measurement, the representative and informative contexts are selected to construct the target corpus set by an active learning approach. Secondly, the domain contextual relevance of candidate entities is calculated by using Domain the discrimination degree and domain dependence function for recognizing the target entities. Experimental results show that the proposed method can effectively reduce training time and improve the accuracy of entity recognition.

Keyword:

biomedical named entity recognition active learning narrow-domain entity domain relevance measurement

Author Community:

  • [ 1 ] [Gao, Jiangfan]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 2 ] [Chen, Jianhui]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 3 ] [Zhang, Shun]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 4 ] [He, Xiaobo]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 5 ] [Lin, Shaofu]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 6 ] [Chen, Jianhui]Beijing Univ Technol, Beijing Inst Smart City, Beijing, Peoples R China
  • [ 7 ] [Lin, Shaofu]Beijing Univ Technol, Beijing Inst Smart City, Beijing, Peoples R China
  • [ 8 ] [Chen, Jianhui]Beijing Key Lab MRIand Brain Informat, Beijing, Peoples R China
  • [ 9 ] [Lin, Shaofu]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China

Reprint Author's Address:

  • [Gao, Jiangfan]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China

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Source :

PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019)

Year: 2019

Page: 1495-1499

Language: English

Cited Count:

WoS CC Cited Count: 20

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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