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Information Processing and Management: an International Journal
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Question classification with support vector machines and error correcting codes
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Learning question classifiers: the role of semantic information
Natural Language Engineering
Enhanced answer type inference from questions using sequential models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Improving the performance of question answering with semantically equivalent answer patterns
Data & Knowledge Engineering
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
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CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
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Responding correctly to a question given a large collection of textual data is not an easy task. There is a need to perceive and recognize the question at a level that permits to detect some constraints that the question imposes on possible answers. The question classification task is used in Question Answering systems. This deduces the type of expected answer, to perform a semantic classification to the target answer. The purpose is to provide additional information to reduce the gap between answer and question to match them. An approach to ameliorate the effectiveness of classifiers focusing on the linguistic analysis (semantic, syntactic and morphological) and statistical approaches guided by a layered semantic hierarchy of fine grained questions types. This work also proposes two methods of questions expansion. The first finds for each word synonyms matching its contextual sense. The second one adds a high representation "hypernym" for the noun. Various representation features of documents, term weighting and diverse machine learning algorithms are studied. Experiments conducted on actual data are presented. Of interest is the improvement in the precision of the classification of questions.