基于大语言模型增强科学命名实体识别：一种类型驱动的多任务学习方法
Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach
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中文总结 AI 辅助
该研究针对LLMs处理SciNER时因实体类型过多导致准确率低的问题，提出类型驱动的多任务学习方法TdSciNER，通过实体类型筛选、多任务学习和示例选择策略提升性能，相关方法在三个数据集上达到与全监督模型相当的效果。
中文摘要 AI 辅助
科学命名实体识别（SciNER）在从科学文本中进行信息提取和知识发现方面发挥着至关重要的作用。近年来，大语言模型（LLMs）已展现出仅需极少人力就能实现具有竞争力的SciNER性能的能力。现有研究强调，将候选实体类型信息纳入对LLMs进行准确的实体识别和分类十分重要。然而，当在提示中提供过多候选实体类型时，LLMs难以准确识别和标注科学文本中的实体，科学文本中的实体类型比通用领域更为复杂。为应对这一挑战，我们提出了TdSciNER，一种类型驱动的方法，可有效利用实体类型信息来提升SciNER性能。在TdSciNER中，我们首先设计一个实体类型筛选模型，以识别给定句子中存在的最可能的实体类型。随后，我们在多任务学习框架内，与SciNER并行引入一个辅助的多类别实体类型任务，以获取更丰富的上下文表示。接着，我们开发了一种基于句子相似度和实体类型多样性的新型示例选择策略，以激活LLMs的上下文学习能力，从而提升不同科学领域的实体识别准确率。在三个数据集上的实验表明，我们的方法取得了与全监督模型相当的性能。进一步分析验证了TdSciNER中每个实体类型驱动的组件均对SciNER性能的提升有所贡献。本研究为SciNER的未来进展以及更广泛的科学文本挖掘信息提取任务提供了宝贵见解。
英文摘要
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.
发表机构
- Nanjing University of Science and Technology（南京理工大学）
机构由 AI 辅助整理，请以论文原文为准。
