Entity Linking Method Based on Longformer and Comparative Learning

实体链接是自然语言处理中的核心任务，旨在将文本中的实体与知识库中的标准实体进行精确匹配。为了应对传统的实体链接方法在长文本处理时难以捕捉长距离依赖关系，本文提出了一种基于Longformer和对比学习的端到端实体链接方法，用更加精细化的模块设计来专门优化长文本中的实体链接性能。通过融合Longformer在长序列文本处理中的优势与对比学习在优化实体匹配过程中的机制，设计了一个联合优化实体识别和链接任务的框架，从而有效提升了模型在复杂文本中的表现。在实验验证中，本文采用了AIDA-CoNLL英文标准数据集，结果表明所提出的方法在F1值上相较于现有主流方法有了1.8%至11.8%的显著提升。Entity linking is a core task in natural language processing, aiming to precisely match entities mentioned in text with their corresponding standardized entities in a knowledge base. To address the challenges faced by traditional entity linking methods in capturing long-range dependencies within lengthy texts, this paper proposes an end-to-end entity linking approach based on Longformer and contrastive learning. The method incorporates a more refined module design specifically tailored to optimize entity linking performance in long texts. By leveraging the strengths of Longformer in processing long-sequence texts and the mechanisms of contrastive learning for enhancing entity matching, we design a unified framework that jointly optimizes entity recognition and linking tasks, thereby significantly improving the model’s performance on complex texts. In the experimental evaluation, we utilize the AIDA-CoNLL benchmark dataset, and the results demonstrate that the proposed method achieves a notable improvement in F1 score, ranging from 1.8% to 11.8%, compared to existing state-of-the-art methods.