How NLP Can Improve Question Answering

Olivier Ferret (1) , Brigitte Grau (1) , Martine Hurault-Plantet (1) , Gabriel Illouz (1) , Christian Jacquemin (1) , Laura Monceaux (2) , Isabelle Robba (1) , Anne Vilnat (1)

1 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
2 LINA - Laboratoire d'Informatique de Nantes Atlantique

Answering open-domain factual questions requires Natural Language processing for refining document selection and answer identification. With our system QALC, we have participated to the Question Answering track of the TREC8, TREC9, and TREC10 evaluations. QALC performs an analysis of documents relying on multi-word term search and their linguistic variation both to minimize the number of documents selected and to provide additional clues when comparing question and sentence representations. This comparison process also makes use of the results of a syntactic parsing of the questions and Named Entity recognition functionalities. Answer extraction relies on the application of syntactic patterns chosen according to the kind of information that is sought for, and categorized depending on the syntactic form of the question. These patterns allow QALC to handle nicely linguistic variations at the answer level

Mots clés: Question answering system; terminological variant; extraction pattern; linguistic variation; information retrieval

Domaines: Traitement du texte et du document; Informatique et langage [cs.CL]

Olivier Ferret, Brigitte Grau, Martine Hurault-Plantet, Gabriel Illouz, Christian Jacquemin, et al.. How NLP Can Improve Question Answering. Knowledge Organization, 2002, 29 (3-4), pp.135--155. ⟨hal-00442219⟩