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SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
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The Journal of Machine Learning Research
The FrameNet tagset for frame-semantic and syntactic coding of predicate-argument structure
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The effect of document retrieval quality on factoid question answering performance
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
QUALIFIER: question answering by lexical fabric and external resources
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
SVM answer selection for open-domain question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A noisy-channel approach to question answering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Generic soft pattern models for definitional question answering
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
An SVM based voting algorithm with application to parse reranking
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Boosting-based parse reranking with subtree features
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Question answering as question-biased term extraction: a new approach toward multilingual QA
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Reranking answers for definitional QA using language modeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
HITIQA: towards analytical question answering
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Speeding up training with tree kernels for node relation labeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Exploring syntactic features for relation extraction using a convolution tree kernel
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Structured retrieval for question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Designing an interactive open-domain question answering system
Natural Language Engineering
Kernels on linguistic structures for answer extraction
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Semi-supervised semantic role labeling using the latent words language model
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Hierarchical semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Kernel engineering for fast and easy design of natural language applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Kernel Engineering for Fast and Easy Design of Natural Language Applications
Unified Semantic Role Labeling for Verbal and Nominal Predicates in the Chinese Language
ACM Transactions on Asian Language Information Processing (TALIP)
A survey on question answering technology from an information retrieval perspective
Information Sciences: an International Journal
Exploiting user profile information for answer ranking in cQA
Proceedings of the 21st international conference companion on World Wide Web
Sibyl, a factoid question-answering system for spoken documents
ACM Transactions on Information Systems (TOIS)
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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Building structures from classifiers for passage reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Answer selection is the most complex phase of a question answering (QA) system. To solve this task, typical approaches use unsupervised methods such as computing the similarity between query and answer, optionally exploiting advanced syntactic, semantic or logic representations. In this paper, we study supervised discriminative models that learn to select (rank) answers using examples of question and answer pairs. The pair representation is implicitly provided by kernel combinations applied to each of its members. To reduce the burden of large amounts of manual annotation, we represent question and answer pairs by means of powerful generalization methods, exploiting the application of structural kernels to syntactic/semantic structures. We experiment with support vector machines and string kernels, syntactic and shallow semantic tree kernels applied to part-of-speech tag sequences, syntactic parse trees and predicate argument structures on two datasets which we have compiled and made available. Our results on classification of correct and incorrect pairs show that our best model improves the bag-of-words model by 63% on a TREC dataset. Moreover, such a binary classifier, used as a re-ranker, improves the mean reciprocal rank of our baseline QA system by 13%. These findings demonstrate that our method automatically selects an appropriate representation of question-answer relations.