relation: http://eprints.imtlucca.it/1890/
title: Learning Relatedness Measures for Entity Linking
creator: Ceccarelli, Diego
creator: Lucchese, Claudio
creator: Orlando, Salvatore
creator: Perego, Raffaele
creator: Trani, Salvatore
subject: QA75 Electronic computers. Computer science
subject: QA76 Computer software
description: 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 knowl- edge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant enti- ties selected for annotation, since this minimizes errors in disambiguating entity-linking.  The definition of an e↵ective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of dif- ferent state-of-the-art entity-linking algorithms.
publisher: ACM
date: 2013-10-28
type: Book Section
type: PeerReviewed
format: application/pdf
language: en
identifier: http://eprints.imtlucca.it/1890/1/paper.pdf
identifier:   Ceccarelli, Diego and Lucchese, Claudio and Orlando, Salvatore and Perego, Raffaele and Trani, Salvatore  Learning Relatedness Measures for Entity Linking.   In:  Proceedings of the 22nd ACM international conference on Conference on information & knowledge management.   ACM, pp. 139-148.  ISBN 978-1-4503-2263-8      (2013)  
relation: 10.1145/2505515.2505711