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

Si Shen (), Jiangfeng Liu, Litao Lin, Ying Huang, Lin Zhang, Chang Liu, Yutong Feng and Dongbo Wang
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Si Shen: Nanjing University of Science and Technology
Jiangfeng Liu: Nanjing Agricultural University
Litao Lin: Nanjing Agricultural University
Ying Huang: Wuhan University
Lin Zhang: Wuhan University
Chang Liu: Nanjing Agricultural University
Yutong Feng: Nanjing Agricultural University
Dongbo Wang: Nanjing Agricultural University

Scientometrics, 2023, vol. 128, issue 2, No 16, 1263 pages

Abstract: 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.

Keywords: Social science; Natural language processing; Pre-trained models; Text analysis; BERT (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-022-04602-4

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