Recognizing Named Entities using Automatically Extracted Transduction Rules

Nouvel Damien (1) , Jean-Yves Antoine (2) , Nathalie Friburger (3) , Arnaud Soulet (3)

1 BDTLN - Bases de données et traitement des langues naturelles
2 Lab-STICC_UBS_CACS_MOCS
3 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 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 results about the use of text mining techniques to automatically enrich our system's knowledge base. We exhaustively search for lexico-syntactic patterns, that recognize named entitites boundaries. We assess their efficiency by using such patterns in a standalone mode and in combination with the existing system

Mots clés: Named Entities Recognition; Text Mining; Transduction Rules

Domaines: Informatique et langage [cs.CL]

Nouvel Damien, Jean-Yves Antoine, Nathalie Friburger, Arnaud Soulet. Recognizing Named Entities using Automatically Extracted Transduction Rules. 5th Language and Technology Conference, Nov 2011, Poznan, Poland. pp.136-140. ⟨hal-00664610⟩