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Enlarging drug dictionary with semi-supervised learning for Drug Entity Recognition

  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Drug Entity Recognition (DER) is a crucial task for information extraction in biomedical text. Much of previous work for DER using known drugs to build features, however, the known drug resources are limited. In this paper, we proposed a semi-supervised learning to extend an existing drug dictionary. With the extended dictionary, the features for DER can be enriched. Using Conditional Random Fields (CRF) model with the enriched features, an F-measure of 89.26% is achieved on DDIExtraction2013 challenge data set, which outperforms the best system of the DDIExtraction 2013 challenge.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
EditorsKevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1929-1931
Number of pages3
ISBN (Electronic)9781509016105
DOIs
StatePublished - 17 Jan 2017
Externally publishedYes
Event2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China
Duration: 15 Dec 201618 Dec 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/TerritoryChina
CityShenzhen
Period15/12/1618/12/16

Keywords

  • Context pattern
  • Information extraction
  • Named entity recognition

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