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Underspecified Query Refinement via Natural Language Question Generation
2012
International Conference on Computational Linguistics
Underspecified queries are common in vertical search engines, leading to large result sets that are difficult for users to navigate. In this paper, we show that we can automatically guide users to their target results by engaging them in a dialog consisting of well-formed binary questions mined from unstructured data. We propose a system that extracts candidate attribute-value question terms from unstructured descriptions of records in a database. These terms are then filtered using a Maximum
dblp:conf/coling/SajjadPG12
fatcat:wk2fbifa75eubosz6ivkjxlhqa