用于增量命名实体识别的类型平衡上下文学习
Type-Balanced Contextual Learning for Incremental Named Entity Recognition
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中文总结 AI 辅助
针对增量命名实体识别中基于伪标签方法的偏差上下文问题，提出含句子对学习方案和上下文一致性损失的TBCL方法，经多数据集多设置实验验证有效。
中文摘要 AI 辅助
增量命名实体识别（INER）是信息抽取中的关键任务，强调在非结构化文本中依次识别新实体类型。面对实体类型的持续涌入，INER存在两大核心挑战：灾难性遗忘的普遍问题，以及非实体类型语义的独特偏移问题。基于伪标签的INER方法已被证明能有效应对这些挑战，但存在一个此前被忽视的问题：偏差上下文问题。我们的分析表明，在新句子中，代表旧实体类型的词元的上下文关联，与旧句子中的对应上下文相比，对新实体类型的偏差显著更强。这种倾向加剧了旧知识的退化，同时导致新知识的过拟合。为解决该偏差上下文问题，我们提出类型平衡上下文学习（Type-Balanced Contextual Learning, TBCL）方法，包含句子对学习方案和上下文一致性损失。该方法通过上下文分析为INER提供了新视角。在三个广受认可的数据集的十个INER设置上开展的大量实验，证明了TBCL方法的有效性，凸显其解决基于伪标签的INER方法固有偏差上下文问题的能力。
英文摘要
Incremental Named Entity Recognition (INER) stands as a pivotal task in information extraction, emphasizing the successive identification of new entity types within unstructured text. Faced with the continuous influx of entity types, INER grapples with two significant challenges: the widespread issue of catastrophic forgetting and the unique shift issue of the non-entity type semantics. While pseudo-labeling-based INER methods have proven effective in addressing these challenges, a previously overlooked issue arises: the biased context problem. Our analysis shows that, in new sentences, the contextual associations of tokens representing old entity types exhibit a significantly stronger bias towards new entity types compared to their contexts in old sentences. This tendency intensifies the degradation of old knowledge while promoting the overfitting of new knowledge. To solve this biased context, we propose a Type-Balanced Contextual Learning (TBCL) method, featuring a sentence-duplet learning scheme and a contextual consistency loss. This approach offers a fresh perspective for INER through context analysis. Extensive experiments across ten INER settings on three highly recognized datasets showcase the efficacy of our TBCL method, highlighting its proficiency in resolving the biased context issue inherent in pseudo-labeling based INER approaches.
发表机构
- Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
- Kyoto University（京都大学）
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- Tencent, AI Lab（腾讯人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。
