SsciBert
A pre-trained language model for social science texts
Bibliographic Data
| ID | 21442041 |
|---|---|
| Authors | Si Shen (0009-0003-3554-4307, Nanjing University of Science and Technology, corresponding author), Jiangfeng Liu (0000-0001-7268-7313, Nanjing Agricultural University), Litao Lin (0000-0001-8182-5458, Nanjing Agricultural University), Ying Huang (0000-0003-3569-7576, Wuhan University), Lin Zhang (0000-0001-5966-3827, Wuhan University), Chang Liu (0000-0002-1198-6990, Nanjing Agricultural University), Yutong Feng (0000-0001-5612-8714, Nanjing Agricultural University), Dongbo Wang (0000-0001-6058-9660, Nanjing Agricultural University) |
| Year | 2023 |
| Volume | 128 |
| Issue | 2 |
| Pages | 1241-1263 |
| Publication date | 2023-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-022-04602-4 |
| OpenAlex | W4282049100 |
| Language | EN |
| Citations received | 7 |
| References cited | 29 |
Citation · Civilization · Data science · Domain (mathematical analysis) · Function (biology) · Information retrieval · Named-entity recognition · Natural language processing · World Wide Web · Artificial Intelligence · Computational and Text Analysis Methods · Computer Science · Natural Language Processing Techniques · Topic Modeling
Highlighting significance in doctoral thesis conclusions
Predicting the impact of scientific articles through semantic analysis of abstracts
Journal article classification using abstracts
Enhancing scientific literature summarization via contrastive learning and chain-of-thought prompting
Research on classification prediction method based on gradient saliency text feature extraction
Local citation relationship classification for recommendation
Using Large Language Models to Assess Equity in U.S. Local Government American Rescue Plans
| Citations per year | 7 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |