Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach

2026-08-09

AI Summary

This work addresses the challenge of named entity recognition (NER) in scientific texts, where the large number of candidate entity types often hinders the performance of large language models. To mitigate this type overload issue, the authors propose TdSciNER, a type-driven multi-task learning framework that incorporates an auxiliary entity classification task. The approach further integrates a context example selection strategy based on sentence similarity and type diversity to enhance model generalization. Evaluated on three scientific text datasets, TdSciNER achieves performance comparable to fully supervised models, demonstrating that type filtering combined with the proposed example selection mechanism plays a crucial role in improving NER accuracy.

Abstract

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 type-driven approach that effectively leverages entity type information to enhance SciNER performance. In TdSciNER, we first design an entity type filter model to identify the most likely entity types present in a given sentence. Subsequently, we introduce an auxiliary multi-class entity typing task within a multi-task learning framework alongside SciNER to obtain richer contextual representations. Then, we develop a novel demonstration selection strategy based on sentence similarity and entity type diversity to activate the in-context learning capabilities of LLMs, thereby improving entity recognition accuracy across diverse scientific domains. Experiments on three datasets demonstrate that our method achieves performance comparable to fully supervised models. Further analysis validates that each entity type-driven component in TdSciNER contributes to the improvement of SciNER performance. This work provides valuable insights for future advancements in SciNER and broader information extraction tasks in scientific text mining.

Problem

• Scientific Named Entity Recognition
• Large Language Models
• Entity Type Complexity
• Prompt-based Learning
• Scientific Text Mining

Innovation

• type-driven
• multi-task learning
• large language models
• scientific named entity recognition
• in-context learning