A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Discovering key concepts in verbose queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Regression Rank: Learning to Meet the Opportunity of Descriptive Queries
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Reducing long queries using query quality predictors
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning concept importance using a weighted dependence model
Proceedings of the third ACM international conference on Web search and data mining
Exploring reductions for long web queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Improving verbose queries using subset distribution
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Rewriting null e-commerce queries to recommend products
Proceedings of the 21st international conference companion on World Wide Web
Generating reformulation trees for complex queries
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Modeling reformulation using query distributions
ACM Transactions on Information Systems (TOIS)
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Improving verbose (or long) queries poses a new challenge for search systems. Previous techniques mainly focused on two aspects, weighting the important words or phrases and selecting the best subset query. The former does not consider how words and phrases are used in actual subset queries, while the latter ignores alternative subset queries. Recently, a novel reformulation framework has been proposed to transform the original query as a distribution of reformulated queries, which overcomes the disadvantages of previous techniques. In this paper, we apply this framework to verbose queries, where a reformulated query is specified as a subset query. Experiments on TREC collections show that the query distribution based framework outperforms the state-of-the-art techniques.