Pattern Mining for Named Entity Recognition

Damien Nouvel (1) , Jean-Yves Antoine (2) , Nathalie Friburger (2)

1 BDTLN - Bases de données et traitement des langues naturelles
2 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours

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.

Mots clés: Named Entity Recognition; text mining; hierarchical pattern mining; sequential patterns

Domaines: Informatique et langage [cs.CL]

Damien Nouvel, Jean-Yves Antoine, Nathalie Friburger. Pattern Mining for Named Entity Recognition. 5th Language and Technology Conference, LTC 2011, Poznan, Poland, Nov 2011, Poznan, Poland. pp.226-237, ⟨10.1007/978-3-319-08958-419⟩. ⟨hal-01064385⟩