Document (#26851)

Author

Ferret, O.
Grau, B.
Hurault-Plantet, M.
Illouz, G.
Jacquemin, C.
Monceaux, L.
Robba, I.
Vilnat, A.

Title

How NLP can improve question answering

Source

Knowledge organization. 29(2002) nos.3/4, S.135-155

Year

2002

Abstract

Answering open-domain factual questions requires Natural Language processing for refining document selection and answer identification. With our system QALC, we have participated in the Question Answering track of the TREC8, TREC9 and TREC10 evaluations. QALC performs an analysis of documents relying an multiword term searches 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 an the application of syntactic patterns chosen according to the kind of information that is sought, and categorized depending an the syntactic form of the question. These patterns allow QALC to handle nicely linguistic variations at the answer level.

Theme

Computerlinguistik
Retrievalstudien
Sprachretrieval

Object

TREC

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