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, and introduce our methodology for evaluation. Our preliminary results show that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve retrieval effectiveness.
Type de document :
Communication dans un congrès
the 23rd Annual ACM Symposium on Applied Computing, Mar 2008, Fortaleza, Brazil. 2008
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Contributeur : Anne-Marie Vercoustre <>
Soumis le : mardi 20 novembre 2007 - 12:35:10
Dernière modification le : vendredi 25 mai 2018 - 12:02:04
Document(s) archivé(s) le : lundi 12 avril 2010 - 02:48:41


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  • HAL Id : inria-00189149, version 1
  • ARXIV : 0711.3128



Anne-Marie Vercoustre, James Thom, Jovan Pehcevski. Entity Ranking in Wikipedia. the 23rd Annual ACM Symposium on Applied Computing, Mar 2008, Fortaleza, Brazil. 2008. 〈inria-00189149〉



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