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Communication Dans Un Congrès Année : 2011

Selecting answers to questions from Web documents by a robust validation process

Résumé

Question answering (QA) systems aim at finding answers to question posed in natural language using a collection of documents. When the collection is extracted from the Web, the structure and style of the texts are quite different from those of newspaper articles. We developed a QA system based on an answer validation process able to handle Web specificity. A large number of candidate answers are extracted from short passages in order to be validated according to question and passages characteristics. The validation module is based on a machine learning approach. It takes into account criteria characterizing both the passage and answer relevance at the surface, lexical, syntactic and semantic levels to deal with different types of texts. We present and compare results obtained for factual questions posed on a Web and on a newspaper collection. We show that our system outperforms a baseline by up to 48% in MRR.
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Dates et versions

hal-02282060 , version 1 (09-09-2019)

Identifiants

  • HAL Id : hal-02282060 , version 1

Citer

Arnaud Grappy, Brigitte Grau, Mathieu-Henri Falco, Anne-Laure Ligozat, Isabelle Robba, et al.. Selecting answers to questions from Web documents by a robust validation process. IEEE/WIC/ACM International Conference on Web Intelligence, Jan 2011, Lyon, France. ⟨hal-02282060⟩
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