SEL

Abstract

The Entity Linking task consists in automatically identifying and linking the entities mentioned in a text to their URIs in a given Knowledge Base, e.g., Wikipedia. Entity Linking has a large im- pact in several text analysis and information retrieval related tasks. This task is very challenging due to natural language ambiguity. However, not all the entities mentioned in a document have the same relevance and utility in understanding the topics being dis- cussed. Thus, the related problem of identifying the most relevant entities present in a document, also known as Salient Entities, is attracting increasing interest. In this paper we propose SEL, a novel supervised two-step algo- rithm comprehensively addressing both entity linking and saliency detection. The first step is based on a classifier aimed at identi- fying a set of candidate entities that are likely to be mentioned in the document, thus maximizing the precision of the method with- out hindering its recall. The second step is still based on machine learning, and aims at choosing from the previous set the entities that actually occur in the document. Indeed, we tested two dif- ferent versions of the second step, one aimed at solving only the entity linking task, and the other that, besides detecting linked en- tities, also scores them according to their saliency. Experiments conducted on two different datasets show that the proposed algo- rithm outperforms state-of-the-art competitors, and is able to detect salient entities with high accuracy.