缓解跨领域命名实体识别中的实体类型混淆：基于多维量化与推理增强
Mitigating Entity Type Confusion in Cross-Domain NER via Multidimensional Quantification and Reasoning Enhancement
- Beijing Institute of Technology（北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对跨领域命名实体识别中的实体类型混淆问题，提出多维混淆量化模型与渐进式双向推理链，利用大语言模型推理增强知识，在CrossNER数据集上取得最先进结果。
AI中文摘要：
跨领域命名实体识别（CD-NER）旨在将源领域的丰富知识迁移到目标领域。近期采用分解或生成范式的研究取得了显著的性能提升，在实体跨度检测方面展现出高准确性。然而，在实体类型分类过程中，模型严重遭受实体类型混淆问题，即模型倾向于将文本中某一类型的实体错误地分类为另一种相似但不正确的类型。为解决此问题，我们首先提出一种多维混淆量化模型（MCQM），该模型从三个维度量化模型在实体类型之间的混淆程度：源-目标层次分析、语义相似性分析和显式数据评估。此外，我们提出了渐进式双向推理链（PBRC）。PBRC利用MCQM中的源-目标层次和混淆分析来提示大语言模型生成两阶段推理信息。两阶段推理信息用于增强模型的知识，显著缓解实体类型混淆并提高模型的泛化性能。实验结果表明，我们的方法在CrossNER数据集的所有领域上均取得了新的最先进结果。
英文摘要：
Cross-domain Named Entity Recognition (CD-NER) aims to transfer the rich knowledge in the source domain to the target domain. Recent studies adopting decomposition or generation paradigms have achieved significant performance improvements, demonstrating high accuracy in entity span detection. However, during entity type classification, models severely suffer from entity type confusion, the erroneous tendency that models classify entities of one type in the text as another similar but incorrect type. To address this issue, we first propose a Multidimensional Confusion Quantification Model (MCQM) that quantifies a model's confusion extent between entity types from three dimensions: source-target hierarchy analysis, semantic similarity analysis, and explicit data evaluation. Moreover, we propose the Progressive Bidirectional Reasoning Chain (PBRC). PBRC leverages the source-target hierarchy and confusion analysis from the MCQM to prompt the LLM to generate two-stage reasoning information. The two-stage reasoning information is utilized to augment the knowledge of the model, significantly mitigating entity type confusion and improving the model's generalization performance. Experimental results demonstrate that our method achieves new state-of-the-art results on all domains of the CrossNER dataset.
