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International Symposium on Emerging Technologies for Education

SETE 2017: Emerging Technologies for Education pp 518–527Cite as

A CRFs-Based Approach Empowered with Word Representation Features to Learning Biomedical Named Entities from Medical Text

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10676))

Abstract

Targeting at identifying specific types of entities, biomedical named entity recognition is a fundamental task of biomedical text processing. This paper presents a CRFs-based approach to learning disease entities by identifying their boundaries in texts. Two types of word representation features are proposed and used including word embedding features and cluster-based features. In addition, an external disease dictionary feature is also explored in the learning process. Based on a publically available NCBI disease corpus, we evaluate the performance of the CRFs-based model with the combination of these word representation features. The results show that using these features can significantly improve BNER performance with an increase of 24.7% on F1 measure, demonstrating the effectiveness of the proposed features and the feature-empowered approach.

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Notes

  1. 1.

    http://taku910.github.io/crfpp/.

  2. 2.

    https://www.ncbi.nlm.nih.gov/pubmed/.

  3. 3.

    http://www.nactem.ac.uk/GENIA/tagger/.

  4. 4.

    https://nlp.stanford.edu/projects/glove/.

  5. 5.

    https://github.com/percyliang/brown-cluster/.

  6. 6.

    https://www.pharmgkb.org/downloads/.

  7. 7.

    For 2013 NIH corpus, it takes about 19 h to cluster words into 500 clusters, but 89 h into 1000 clusters on a machine with i5 CPU and 8 GB memory.

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Acknowledgements

The work was substantially supported by the National Natural Science Foundation of China (Nos. 61572145 and 61403088), the Frontier and Key Technology Innovation Special Grant of Guangdong Province (No. 2014B010118005), the Public Interest Research and Capability Building Grant of Guangdong Province (No. 2014A020221039), and the Innovative School Project in Higher Education of Guangdong Province (No.YQ2015062).

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Authors and Affiliations

  1. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China

    Wenxiu Xie, Sihui Fu, Shengyi Jiang & Tianyong Hao

  2. Collaborative Innovation Center for 21st-Century Maritime Silk Road Studies, Guangdong University of Foreign Studies, Guangzhou, China

    Tianyong Hao

Authors
  1. Wenxiu Xie
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  2. Sihui Fu
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  3. Shengyi Jiang
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  4. Tianyong Hao
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Corresponding authors

Correspondence to Shengyi Jiang or Tianyong Hao .

Editor information

Editors and Affiliations

  1. National Taichung University of Science and Technology, Taichung City, Taiwan

    Tien-Chi Huang

  2. City University of Hong Kong, Kowloon, Hong Kong

    Rynson Lau

  3. Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan

    Yueh-Min Huang

  4. University of Caen Normandie, Caen, France

    Marc Spaniol

  5. City University of Hong Kong, Kowloon, Hong Kong

    Chun-Hung Yuen

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Xie, W., Fu, S., Jiang, S., Hao, T. (2017). A CRFs-Based Approach Empowered with Word Representation Features to Learning Biomedical Named Entities from Medical Text. In: Huang, TC., Lau, R., Huang, YM., Spaniol, M., Yuen, CH. (eds) Emerging Technologies for Education. SETE 2017. Lecture Notes in Computer Science(), vol 10676. Springer, Cham. https://doi.org/10.1007/978-3-319-71084-6_61

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  • DOI: https://doi.org/10.1007/978-3-319-71084-6_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71083-9

  • Online ISBN: 978-3-319-71084-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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