Making large-scale support vector machine learning practical
Advances in kernel methods
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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Answer typing for information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Leveraging community-built knowledge for type coercion in question answering
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Locational relativity and domain constraints in spatial questions
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Acquisition of open-domain classes via intersective semantics
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An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What ...? and Which ...? questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost 0.2 in MRR for both the first appropriate and first correct answers is observed along with an increase in precision over the entire range of recall.