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Mathematical Subject Information Entity Recognition Method Based on BiLSTM-CRF

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Machine Learning for Cyber Security (ML4CS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12488))

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Abstract

Combining language conditional random field (CRF) and bidirectional long-term and short-term memory (BiLSTM) networks, a mathematical subject information entity recognition method based on BiLSTM-CRF is constructed to extract entity information in mathematical language. Experimental results show that compared with BiLSTM, BiLSTM-CRF improves the recall rate by nearly 5%, the accuracy rate by nearly 2%, and the F1 value by nearly 4%. The results of the BERT-CRF model are also significantly better than other models.

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Acknowledgments

We thank the anonymous reviewers. This work is supported by Doctoral Research launch project of Yunnan Normal University (No. 2019XJLK21), and Program for innovative research team (in Science and Technology) in University of Yunnan Province.

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Authors and Affiliations

  1. School of Information Science and Technology, Yunnan Normal University, Kunming, China

    Haoze Li

  2. Key Laboratory of Education Informalization for Nationalities, Yunnan Normal University, Kunming, China

    Haoze Li, Tianwei Xu & Juxiang Zhou

  3. Yunnan Key Laboratory of Smart Education, Yunnan Normal University, Kunming, 650500, China

    Tianwei Xu & Juxiang Zhou

Authors
  1. Haoze Li
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  2. Tianwei Xu
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  3. Juxiang Zhou
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Corresponding author

Correspondence to Tianwei Xu .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Xiaofeng Chen

  2. Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Michigan State University, East Lansing, MI, USA

    Qiben Yan

  4. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science, Thuwal, Saudi Arabia

    Xiangliang Zhang

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Li, H., Xu, T., Zhou, J. (2020). Mathematical Subject Information Entity Recognition Method Based on BiLSTM-CRF. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_24

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  • DOI: https://doi.org/10.1007/978-3-030-62463-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62462-0

  • Online ISBN: 978-3-030-62463-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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