In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer model identifies surface forms (entity mentions) in a given text. For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list. The latter transformer is fed by an enriched context captured from the sentence (i.e. local context), and entity description gained from Wikipedia. Such external contexts have not been used in the state of the art EL approaches. Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.

翻译：在本文中,我们提出CHOLAN,这是针对知识基础的端到端实体连接(EL)的模块化方法;CHOLAN由两种基于变压器的模型组成管道,它们相继结合完成EL任务;第一种变压器模型在给定文本中确定了表面形式(实体提及);对于每一种情况,都使用第二个变压器模型将目标实体划入预先确定的候选人名单;后一种变压器由从句子中捕获的丰富背景(即当地背景)和从维基百科获得的实体描述所提供。这种外部环境在先进的EL方法中并未使用。我们的经验性研究是在两个众所周知的知识库(即维基数据和维基百科)进行的。经验性结果表明,CHOLAN在标准数据集(如ConNLL-AIDA、MSNBC、AQUINT、ACE2004和T-REx)上,超越了最新的最新方法。