PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Ceccarelli D., Lucchese C., Orlando S., Perego R., Trani S. Learning relatedness measures for entity linking. In: CIKM'2013 - 22nd ACM International Conference on Information & Knowledge Management (San Francisco, Usa, 27 October - 1 November 2013). Proceedings, pp. 139 - 148. ACM, 2013.
 
 
Abstract
(English)
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. The definition of an effective 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 different state-of-the-art entity-linking algorithms.
URL: http://dl.acm.org/citation.cfm?id=2505711
DOI: 10.1145/2505515.2505711
Subject Entity
Relatedness
Learning to rank
Gradient boosting regression tree
Entity linking
H.3.m INFORMATION STORAGE AND RETRIEVAL. Miscellabeous
68P20


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