Papers › SsciBERT: A Pre-trained Language Model for Social Science Texts
SsciBERT: A Pre-trained Language Model for Social Science Texts
Si Shen, Jiangfeng Liu, Litao Lin, Ying Huang, Lin Zhang, Chang Liu, Yutong Feng, Dongbo Wang
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.
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