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Chinese Governmental Named Entity Recognition

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Book cover Information Retrieval Technology (AIRS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11292))

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Abstract

Named entity recognition (NER) is a fundamental task in natural language processing and there is a lot of interest on vertical NER such as medical NER, short text NER etc. In this paper, we study the problem of Chinese governmental NER (CGNER). CGNER serves as the basis for automatic governmental text analysis, which can greatly benefit the public. Considering the characteristics of the governmental text, we first formulate the task of CGNER, adding one new entity type, i.e., policy (POL) in addition to the generic types such as person (PER), location (LOC), organization (ORG) and title (TIT) for recognition. Then we constructed a dataset called GOV for CGNER. We empirically evaluate the performances of mainstream NER tools and state-of-the-art BiLSTM-CRF method on the GOV dataset. It was found that there is a performance decline compared to applying these methods on generic NER dataset. Further studies show that compound entities account for a non-negligible proportion and using the classical BIO (Begin-Inside-Outside) annotation cannot encode the entity type combination effectively. To alleviate the problem, we propose to utilize the compound tagging and BiLSTM-CRF for doing CGNER. Experiments show that our proposed methods can significantly improve the CGNER performance, especially for the LOC, ORG and TIT entity types.

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Notes

  1. 1.

    http://www.xinhuanet.com/.

  2. 2.

    http://www.gov.cn/.

  3. 3.

    https://gate.ac.uk/projects.html.

  4. 4.

    http://www.people.com.cn/.

  5. 5.

    https://nlp.stanford.edu/software/CRF-NER.shtml.

  6. 6.

    https://www.ltp-cloud.com/.

  7. 7.

    https://radimrehurek.com/gensim/.

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Acknowledgment

We would like to thank the anonymous reviewers for their insightful comments and suggestions. This research is supported by the The National Key Research and Development Program of China (grant No. 2016YFB0801003 & 2017YFB0803301).

Author information

Authors and Affiliations

  1. Institute of Information Engineering Chinese Academy of Sciences, Beijing, China

    Qi Liu, Dong Wang, Meilin Zhou, Peng Li, Baoyuan Qi & Bin Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Qi Liu, Dong Wang, Meilin Zhou, Peng Li, Baoyuan Qi & Bin Wang

Authors
  1. Qi Liu
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  2. Dong Wang
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  3. Meilin Zhou
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  4. Peng Li
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  5. Baoyuan Qi
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  6. Bin Wang
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Corresponding author

Correspondence to Dong Wang .

Editor information

Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Yuen-Hsien Tseng

  2. Waseda University, Tokyo, Japan

    Tetsuya Sakai

  3. School of Information Systems, Singapore Management University, Singapore, Singapore

    Jing Jiang

  4. Academia Sinica, Taipei, Taiwan

    Lun-Wei Ku

  5. Huawei Research America, Champaign, IL, USA

    Dae Hoon Park

  6. National Chiayi University, Chiayi City, Taiwan

    Jui-Feng Yeh

  7. Yuan Ze University, Taoyuan City, Taiwan

    Liang-Chih Yu

  8. National Central University, Taoyuan City, Taiwan

    Lung-Hao Lee

  9. National Taiwan Normal University, Taipei City, Taiwan

    Zhi-Hong Chen

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Liu, Q., Wang, D., Zhou, M., Li, P., Qi, B., Wang, B. (2018). Chinese Governmental Named Entity Recognition. In: Tseng, YH., et al. Information Retrieval Technology. AIRS 2018. Lecture Notes in Computer Science(), vol 11292. Springer, Cham. https://doi.org/10.1007/978-3-030-03520-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-03520-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03519-8

  • Online ISBN: 978-3-030-03520-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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