Exploiting Relevant Hyperlinks in Knowledge Base for Entity Linking

In this study, we propose a new model aiming to enhance the quality of entity linking by exploiting highly relevant hyperlinks in knowledge base for entity disambiguation. We find that most existing studies do not filter the corresponding hyperlinks for each entity, where using the irrelevant ones may introduce noises and lower the linking accuracy. To address this issue, we design and combine the hyperlink extraction stage and the hyperlink attention stage to learn more suitable hyperlinks in the document-level disambiguation. In addition, we also enhance the context-level disambiguation by utilizing additional entity descriptions and work on retrieving high-quality candidate set for entities at the beginning of our model. Experimental results show that our proposed model outperforms the state-of-the-arts on various benchmark datasets, and even being competitive to the models that rely on additional information.