Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Relational Learning for NLP using Linear Threshold Elements
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
Two Level Question Classification Based on SVM and Question Semantic Similarity
ICECT '09 Proceedings of the 2009 International Conference on Electronic Computer Technology
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
AcroDef: a quality measure for discriminating expansions of ambiguous acronyms
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
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Most question and answering systems are based on three research themes: question classification and analysis, document retrieval and answer extraction. The performance in every stage affects the final result. To respond correctly to a question given a large collection of textual data is not an easy task. There is a need to perceive and recognise the question at a level that permits to detect some constraints that the question imposes on possible answers. The classification of questions appears as an important task because it deduces the type of expected answers. The purpose is to provide additional information to reduce the gap between answer and question. A method to improve the performance of question classification focusing on linguistic analysis and statistical approaches is presented. This work also proposes two methods of questions expansion. Various questions representation, term weighting and diverse machine learning algorithms are studied. Experiments conducted on actual data are presented. Of interest is the improvement in the precision on the classification of questions.