Reducing Long Queries Using Query Quality Predictors

Reducing Long Queries Using Query Quality Predictors

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by Giridhar Kumaran , Vitor R. Carvalho

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Datum	Value	Source
TITLE	Reducing Long Queries Using Query Quality Predictors	SVM HeaderParse 0.2
AUTHOR NAME	Giridhar Kumaran	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Corporation; One Microsoft Way	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052, USA	SVM HeaderParse 0.2
AUTHOR NAME	Vitor R. Carvalho	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Corporation; One Microsoft	SVM HeaderParse 0.2
AUTHOR ADDR	Way; Redmond, WA 98052, USA	SVM HeaderParse 0.2
ABSTRACT	Long queries frequently contain many extraneous terms that hinder retrieval of relevant documents. We present techniques to reduce long queries to more effective shorter ones that lack those extraneous terms. Our work is motivated by the observation that perfectly reducing long TREC description queries can lead to an average improvement of 30 % in mean average precision. Our approach involves transforming the reduction problem into a problem of learning to rank all sub-sets of the original query (sub-queries) based on their predicted quality, and select the top sub-query. We use various measures of query quality described in the literature as features to represent sub-queries, and train a classifier. Replacing the original long query with the top-ranked subquery chosen by the ranking classifier results in a statistically significant average improvement of 8 % on our test sets. Analysis of the results shows that query reduction is wellsuited for moderately-performing long queries, and a small set of query quality predictors are well-suited for the task of ranking sub-queries.	SVM HeaderParse 0.2
CITATIONS	29 found	ParsCit 1.0