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1 BDTLN - Bases de données et traitement des langues naturelles LI - Laboratoire d-Informatique de l-Université de Tours 2 LI - Laboratoire d-Informatique de l-Université de Tours

Abstract : Many evaluation campaigns have shown that knowledge-based and data-driven approaches remain equally competitive for Named Entity Recognition. Our research team has developed CasEN, a symbolic system based on finite state tranducers, which achieved promising results during the Ester2 French-speaking evaluation campaign. Despite these encouraging results, manually extending the coverage of such a hand-crafted system is a difficult task. In this paper, we present a novel approach based on pattern mining for NER and to supplement our system-s knowledge base. The system, mXS, exhaustively searches for hierarchical sequential patterns, that aim at detecting Named Entity boundaries. We assess their efficiency by using such patterns in a standalone mode and in combination with our existing system.

Keywords : Named Entity Recognition text mining hierarchical pattern mining sequential patterns

Autor: Damien Nouvel - Jean-Yves Antoine - Nathalie Friburger -



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