@incollection{eprints1890,
           title = {Learning Relatedness Measures for Entity Linking},
          author = {Diego Ceccarelli and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Salvatore Trani},
       booktitle = {Proceedings of the 22nd ACM international conference on Conference on information \& knowledge management},
            year = {2013},
           month = {October},
       publisher = {ACM},
           pages = {139--148},
        keywords = {Entity linking, relatedness measures, learning to rank},
             url = {http://eprints.imtlucca.it/1890/},
        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 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.}
}