Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Answering complex questions with random walk models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Experiments with interactive question-answering
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning to recognize features of valid textual entailments
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
FERRET: interactive question-answering for real-world environments
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Multi-candidate reduction: Sentence compression as a tool for document summarization tasks
Information Processing and Management: an International Journal
Complex question answering: unsupervised learning approaches and experiments
Journal of Artificial Intelligence Research
Automatic question generation for learning evaluation in medicine
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
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This paper proposes a supervised approach for automatically learning good decompositions of complex questions. The training data generation phase mainly builds on three steps to produce a list of simple questions corresponding to a complex question: i) the extraction of the most important sentences from a given set of relevant documents (which contains the answer to the complex question), ii) the simplification of the extracted sentences, and iii) their transformation into questions containing candidate answer terms. Such questions, considered as candidate decompositions, are manually annotated (as good or bad candidates) and used to train a Support Vector Machine (SVM) classifier. Experiments on the DUC data sets prove the effectiveness of our approach.