Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
ACM SIGIR Forum
A question answering system supported by information extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Toward semantics-based answer pinpointing
HLT '01 Proceedings of the first international conference on Human language technology research
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
The structure and performance of an open-domain question answering system
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Automatic web query classification using labeled and unlabeled training data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Automatic Query Classification via Semi-Supervised Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating discourse-based answer extraction for why-question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring question subjectivity prediction in community QA
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Question classification using head words and their hypernyms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Subtree mining for question classification problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Context aware query classification using dynamic query window and relationship net
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Function-based question classification for general QA
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Improving context-aware query classification via adaptive self-training
Proceedings of the 20th ACM international conference on Information and knowledge management
Understanding user intent in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Direct answers for search queries in the long tail
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An evaluation of classification models for question topic categorization
Journal of the American Society for Information Science and Technology
Why question answering using sentiment analysis and word classes
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Question analysis: how watson reads a clue
IBM Journal of Research and Development
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Web searches are increasingly formulated as natural language questions, rather than keyword queries. Retrieving answers to such questions requires a degree of understanding of user expectations. An important step in this direction is to automatically infer the type of answer implied by the question, e.g., factoids, statements on a topic, instructions, reviews, etc. Answer Type taxonomies currently exist for factoid-style questions, but not for open-domain questions. Building taxonomies for non-factoid questions is a harder problem since these questions can come from a very broad semantic space. A few attempts have been made to develop taxonomies for non-factoid questions, but these tend to be too narrow or domain specific. In this paper, we address this problem by modeling the Answer Type as a latent variable that is learned in a data-driven fashion, allowing the model to be more adaptive to new domains and data sets. We propose approaches that detect the relevance of candidate answers to a user question by jointly 'clustering' questions according to the hidden variable, and modeling relevance conditioned on this hidden variable. In this paper we propose 3 new models: (a) Logistic Regression Mixture (LRM), (b) Glocal Logistic Regression Mixture (G-LRM) and (c) Mixture Glocal Logistic Regression Mixture (MG-LRM) that automatically learn question-clusters and cluster-specific relevance models. All three models perform better than a baseline relevance model that uses explicit Answer Type categories predicted by a supervised Answer-Type classifier, on a newsgroups dataset. Our models also perform better than a baseline relevance model that does not use any answer-type information on a blogs dataset.