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Lyon
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-1-4577-1373-6
pp: 55-62
ABSTRACT
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 passage and answer relevance at 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.
INDEX TERMS
fine-grained information retrieval, question-answering system, answer validation, Web document analysis
CITATION
A. Grappy, B. Grau, M-H. Falco, A-L. Ligozat, I. Robba, A. Vilnat, "Selecting Answers to Questions from Web Documents by a Robust Validation Process", WI-IAT, 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies 2011, pp. 55-62, doi:10.1109/WI-IAT.2011.210
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