A symbolic approach to automatic multiword term structuring.

Eric Sanjuan (1) , James Dowdall (2) , Fidelia Ibekwe-Sanjuan (3) , Fabio Rinaldi (4)

1 LIA - Laboratoire Informatique d'Avignon
2 Department of Informatics [Sussex]
3 ELICO - Equipe de recherche de Lyon en sciences de l'information et de la communication
4 Institute of Computational Linguistics

This paper presents a three-level structuring of multiword terms (MWTs) basing on lexical inclusion, WordNet similarity and a clustering approach. Term cluster- ing by automatic data analysis methods offers an interesting way of organizing a domain's knowledge structures, useful for several information-oriented tasks like science and technology watch, textmining, computer-assisted ontology population, Question Answering(Q-A). This paper explores how this three-level term structuring brings to light the knowledge structures from a corpus of genomics and compares the mapping of the domain topics against a hand-built ontology (the GENIA ontology). Ways of integrating the results into a Q-A system are discussed.

Mots clés: Term variation; Automatic terminology structuring; Clustering technique; Topic maps; Knowledge discovery; Question Answering.; Question Answering

Domaines: Sciences de l'information et de la communication; Traitement du texte et du document

Eric Sanjuan, James Dowdall, Fidelia Ibekwe-Sanjuan, Fabio Rinaldi. A symbolic approach to automatic multiword term structuring.. Computer Speech and Language, 2005, 19 (4), pp.524-542. ⟨10.1016/j.csl.2005.02.002⟩. ⟨hal-00636158⟩