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SsciBERT: a pre-trained language model for social science texts

Author

Listed:
  • Si Shen

    (Nanjing University of Science and Technology)

  • Jiangfeng Liu

    (Nanjing Agricultural University)

  • Litao Lin

    (Nanjing Agricultural University)

  • Ying Huang

    (Wuhan University
    Wuhan University)

  • Lin Zhang

    (Wuhan University
    Wuhan University)

  • Chang Liu

    (Nanjing Agricultural University)

  • Yutong Feng

    (Nanjing Agricultural University)

  • Dongbo Wang

    (Nanjing Agricultural University)

Abstract

The academic literature of social sciences records human civilization and studies human social problems. With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals. The models, which are available on GitHub ( https://github.com/S-T-Full-Text-Knowledge-Mining/SSCI-BERT ), show excellent performance on discipline classification, abstract structure–function recognition, and named entity recognition tasks with the social sciences literature.

Suggested Citation

  • Si Shen & Jiangfeng Liu & Litao Lin & Ying Huang & Lin Zhang & Chang Liu & Yutong Feng & Dongbo Wang, 2023. "SsciBERT: a pre-trained language model for social science texts," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1241-1263, February.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:2:d:10.1007_s11192-022-04602-4
    DOI: 10.1007/s11192-022-04602-4
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