NDLI: Survey and evaluation of query intent detection methods

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Survey and evaluation of query intent detection methods

Content Provider	ACM Digital Library
Author	Brenes, David J. Pérez-González, Kilian Gayo-Avello, Daniel
Abstract	User interactions with search engines reveal three main underlying intents, namely navigational, informational, and transactional. By providing more accurate results depending on such query intents the performance of search engines can be greatly improved. Therefore, query classification has been an active research topic for the last years. However, while query topic classification has deserved a specific bakeoff, no evaluation campaign has been devoted to the study of automatic query intent detection. In this paper some of the available query intent detection techniques are reviewed, an evaluation framework is proposed, and it is used to compare those methods in order to shed light on their relative performance and drawbacks. As it will be shown, manually prepared gold-standard files are much needed, and traditional pooling is not the most feasible evaluation method. In addition to this, future lines of work in both query intent detection and its evaluation are proposed.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781605584348
DOI	10.1145/1507509.1507510
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-02-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query intent detection Web search behavior Evaluation Click-through data Msn query log
Content Type	Text
Resource Type	Article

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