Making large-scale support vector machine learning practical
Advances in kernel methods
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Document clustering with committees
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Structured retrieval for question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Flexible answer typing with discriminative preference ranking
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Improving passage retrieval in question answering using NLP
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
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Answer typing is commonly thought of as finding appropriate responses to given questions. We extend the notion of answer typing to information retrieval to ensure results contain plausible answers to queries. Identification of a large class of applicable queries is performed using a discriminative classifier, and discriminative preference ranking methods are employed for the selection of type-appropriate terms. Experimental results show that type-appropriate terms identified by the model are superior to terms most commonly associated with the query, providing strong evidence that answer typing techniques can find meaningful and appropriate terms. Further experiments show that snippets containing correct answers are ranked higher by our model than by the baseline Google search engine in those instances in which a query does indeed seek a short answer.