Entity Ranking in Wikipedia

Abstract : The traditional entity extraction problem lies in the ability of extracting named entities from plain text using natural language processing techniques and intensive training from large document collections. Examples of named entities include organisations, people, locations, or dates. There are many research activities involving named entities; we are interested in entity ranking in the field of information retrieval. In this paper, we describe our approach to identifying and ranking entities from the INEX Wikipedia document collection. Wikipedia offers a number of interesting features for entity identification and ranking that we first introduce. We then describe the principles and the architecture of our entity ranking system. The paper also introduces our methodology for evaluating the effectiveness of entity ranking, as well as preliminary results which show that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve retrieval effectiveness.
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Rapport
[Research Report] RR-6294, INRIA. 2007, pp.8
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https://hal.inria.fr/inria-00172511
Contributeur : Anne-Marie Vercoustre <>
Soumis le : mardi 18 septembre 2007 - 23:47:58
Dernière modification le : vendredi 25 mai 2018 - 12:02:04
Document(s) archivé(s) le : vendredi 25 novembre 2016 - 19:20:49

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Anne-Marie Vercoustre, James Thom, Jovan Pehcevski. Entity Ranking in Wikipedia. [Research Report] RR-6294, INRIA. 2007, pp.8. 〈inria-00172511v2〉

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