Learning relatedness measures for entity linking

This paper formalizes the problem of learning entity relatedness as a learning-to-rank problem, and proposes a methodology to create reference datasets on the basis of manually annotated data.

Abstract

Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowledge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant entities selected for annotation, since this minimizes errors in disambiguating entity-linking.