Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach
Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, large language models (LLMs) have demonstrated the capacity to achieve competitive SciNER performance with minimal human effort. Existing research highlights the importance of incorporating candidate entity type information for accurate entity recognition and classification by LLMs. However, when too many candidate entity types are provided in the prompt, LLMs struggle to accurately recognize and label entities in scientific texts, where entity types are more complex than in general domains. To address this challenge, we propose TdSciNER, a t...
| Authors | Tong Bao, Yi Zhao, Heng Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 August 2026 |
| Last updated at source | 14 August 2026 |
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