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Rule-Based HierarchicalRank: An Unsupervised Approach to Visible Tag Extraction from Semi-structured Chinese Text

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PRICAI 2019: Trends in Artificial Intelligence (PRICAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11672))

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Abstract

The large and growing amounts of semi-structured Chinese text present both challenges and opportunities to enhance text mining and knowledge discovery. One such challenge is to automatically extract a small set of visible tag from a document that can accurately reveal the document’s topic and can facilitate fast information processing. Unfortunately, at this stage, there is still a certain gap between the existing methods and truly engineering application.

In order to narrow this gap, we propose Rule-Based HierarchicalRank (RBH), an unsupervised method for visible tag extraction from semi-structured Chinese text via a documents’ title and non-title two levels. In different level, we use inconsistent methods to extract the candidate visible tags. The experiment results show that the performance of the RBH method is far better than all the baseline methods on visible tag extraction task on two distinct experiment datasets. Specifically, On Paper-Dataset, the rule-based HierarchicalRank methods’ precision and F1-score achieves 18.6% and 14.1%, while TOP K = 5. In addition, on Event-Dataset, the best precision of our method is higher 7% than the state-of-the-art method PositionRank with TOP K = 1. Furthermore, the best Recall of RBH achieves 37.7% when TOP K = 5.

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Notes

  1. 1.

    https://pypi.org/project/jieba/.

  2. 2.

    http://www.jos.org.cn/jos/ch/index.aspx.

  3. 3.

    http://ef.zhiweidata.com/#!/down.

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Acknowledgment

This paper was supported by the National Natural Science Foundation of China (NSFC) via grant No. 61872446 and Natural Science Foundation of Hunan Province, China via grant No. 2018JJ2475 and No. 2018JJ2476.

Author information

Authors and Affiliations

  1. CETC Big Data Research Institute Co., Ltd., Chengdu, China

    Jicheng Lei

  2. Tus-Holdings Co., Ltd., Beijing, China

    Jiali Yu

  3. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan, People’s Republic of China

    Chunhui He, Chong Zhang & Bin Ge

  4. Guizhou Wingscloud Co. Ltd., Guiyang, China

    Yiping Bao

Authors
  1. Jicheng Lei
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  2. Jiali Yu
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  3. Chunhui He
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  4. Chong Zhang
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  5. Bin Ge
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  6. Yiping Bao
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Corresponding author

Correspondence to Chunhui He .

Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Abhaya C. Nayak

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

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Lei, J., Yu, J., He, C., Zhang, C., Ge, B., Bao, Y. (2019). Rule-Based HierarchicalRank: An Unsupervised Approach to Visible Tag Extraction from Semi-structured Chinese Text. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-29894-4_15

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-29894-4

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