IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/vyid10.1007_s10796-019-09932-y.html
   My bibliography  Save this article

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, 0. "TL-NER: A Transfer Learning Model for Chinese Named Entity Recognition," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-019-09932-y
    DOI: 10.1007/s10796-019-09932-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-019-09932-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-019-09932-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Dávid Márk Nemeskey & András Kornai, 2018. "Emergency Vocabulary," Information Systems Frontiers, Springer, vol. 20(5), pages 909-923, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. R. Elakkiya & Pandi Vijayakumar & Marimuthu Karuppiah, 2021. "COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking," Information Systems Frontiers, Springer, vol. 23(6), pages 1369-1383, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Saptarshi Ghosh & Kripabandhu Ghosh & Debasis Ganguly & Tanmoy Chakraborty & Gareth J. F. Jones & Marie-Francine Moens & Muhammad Imran, 2018. "Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(5), pages 901-907, October.
    3. Meng, Qingbin & Huang, Haozheng & Li, Xinyu & Wang, Song, 2023. "Short-selling and corporate default risk: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 398-417.
    4. repec:grz:wpsses:2021-03 is not listed on IDEAS
    5. Bae, Kwangil & Kang, Jangkoo & Lee, Soonhee, 2016. "Bullish/bearish/neutral strategies under short sale restrictions," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 227-239.
    6. Chip Wade & Andre Liebenberg & Benjamin M. Blau, 2016. "Information and Insurer Financial Strength Ratings: Do Short Sellers Anticipate Ratings Changes?," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(2), pages 475-500, June.
    7. Haiyan Jiang & Ahsan Habib & Mostafa Monzur Hasan, 2022. "Short Selling: A Review of the Literature and Implications for Future Research," European Accounting Review, Taylor & Francis Journals, vol. 31(1), pages 1-31, January.
    8. Duong, Truong X. & Huszár, Zsuzsa R. & Yamada, Takeshi, 2015. "The costs and benefits of short sale disclosure," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 124-139.
    9. Merl, Robert & Stöckl, Thomas & Palan, Stefan, 2023. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions," Journal of Banking & Finance, Elsevier, vol. 154(C).
    10. Xufeng Liu & Die Wan, 2022. "Does short‐selling affect mutual fund shareholdings? Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1887-1923, April.
    11. Berkman, Henk & Eugster, Marco, 2017. "Short on drugs: Short selling during the drug development process," Journal of Financial Markets, Elsevier, vol. 33(C), pages 102-123.
    12. Rajat Kumar Behera & Pradip Kumar Bala & Nripendra P. Rana & Hatice Kizgin, 2022. "A Techno-Business Platform to Improve Customer Experience Following the Brand Crisis Recovery: A B2B Perspective," Information Systems Frontiers, Springer, vol. 24(6), pages 2027-2051, December.
    13. Hu, May & Tuilautala, Mataiasi & Yang, Jingjing & Zhong, Qian, 2022. "Asymmetric information and inside management trading in the Chinese market," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    14. Prabhsimran Singh & Surleen Kaur & Abdullah M. Baabdullah & Yogesh K. Dwivedi & Sandeep Sharma & Ravinder Singh Sawhney & Ronnie Das, 2023. "Is #SDG13 Trending Online? Insights from Climate Change Discussions on Twitter," Information Systems Frontiers, Springer, vol. 25(1), pages 199-219, February.
    15. Chen, Jun & Kadapakkam, Palani-Rajan & Yang, Ting, 2016. "Short selling, margin trading, and the incorporation of new information into prices," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 1-17.
    16. Millicent Chang & John Gould & Yuyun Huang & Sirimon Treepongkaruna & Joey Wenling Yang, 2022. "Insider trading and the algorithmic trading environment," International Review of Finance, International Review of Finance Ltd., vol. 22(4), pages 725-750, December.
    17. Comerton-Forde, Carole & Do, Binh Huu & Gray, Philip & Manton, Tom, 2016. "Assessing the information content of short-selling metrics using daily disclosures," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 188-204.
    18. Jyoti Prakash Singh & Abhinav Kumar & Nripendra P. Rana & Yogesh K. Dwivedi, 2022. "Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets," Information Systems Frontiers, Springer, vol. 24(2), pages 459-474, April.
    19. Meng, Qingbin & Li, Xinyu & Chan, Kam C. & Gao, Shenghao, 2020. "Does short selling affect a firm's financial constraints?," Journal of Corporate Finance, Elsevier, vol. 60(C).
    20. Hae Mi Choi, 2020. "Short‐sale constraints and informational efficiency to private information: A natural experiment," The Financial Review, Eastern Finance Association, vol. 55(4), pages 625-643, November.
    21. Gider, Jasmin & Westheide, Christian, 2016. "Relative idiosyncratic volatility and the timing of corporate insider trading," Journal of Corporate Finance, Elsevier, vol. 39(C), pages 312-334.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-019-09932-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.