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TL-NER: A Transfer Learning Model for Chinese Named Entity Recognition

Author

Listed:
  • DunLu Peng

    (University of Shanghai for Science and Technology)

  • YinRui Wang

    (University of Shanghai for Science and Technology)

  • Cong Liu

    (University of Shanghai for Science and Technology)

  • Zhang Chen

    (University of Shanghai for Science and Technology)

Abstract

Most of the current research on Named Entity Recognition (NER) in the Chinese domain is based on the assumption that annotated data are adequate. However, in many scenarios, the sufficient amount of annotated data required for Chinese NER task is difficult to obtain, resulting in poor performance of machine learning methods. In view of this situation, this paper tries to excavate the information contained in the massive unlabeled raw text data and utilize it to enhance the performance of Chinese NER task. A deep learning model combined with Transfer Learning technique is proposed in this paper. This method can be leveraged in some domains where there is a large amount of unlabeled text data and a small amount of annotated data. The experiment results show that the proposed method performs well on different sized datasets, and this method also avoids errors that occur during the word segmentation process. We also evaluate the effect of transfer learning from different aspects through a series of experiments.

Suggested Citation

  • DunLu Peng & YinRui Wang & Cong Liu & Zhang Chen, 2020. "TL-NER: A Transfer Learning Model for Chinese Named Entity Recognition," Information Systems Frontiers, Springer, vol. 22(6), pages 1291-1304, December.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:6:d:10.1007_s10796-019-09932-y
    DOI: 10.1007/s10796-019-09932-y
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    References listed on IDEAS

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    1. Chakrabarty, Bidisha & Shkilko, Andriy, 2013. "Information transfers and learning in financial markets: Evidence from short selling around insider sales," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1560-1572.
    2. Karin Sim Smith & Richard McCreadie & Craig Macdonald & Iadh Ounis, 2018. "Regional Sentiment Bias in Social Media Reporting During Crises," Information Systems Frontiers, Springer, vol. 20(5), pages 1013-1025, October.
    3. Dávid Márk Nemeskey & András Kornai, 2018. "Emergency Vocabulary," Information Systems Frontiers, Springer, vol. 20(5), pages 909-923, October.
    4. Heng-Li Yang & August F. Y. Chao, 2015. "Sentiment analysis for Chinese reviews of movies in multi-genre based on morpheme-based features and collocations," Information Systems Frontiers, Springer, vol. 17(6), pages 1335-1352, December.
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    Cited by:

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