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  1. Proceedings of the 2009 workshop on Web Search Click Data (WSCD '09)
  2. Survey and evaluation of query intent detection methods
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Survey and evaluation of query intent detection methods
Analysis of long queries in a large scale search log
Distinguishing humans from robots in web search logs: preliminary results using query rates and intervals
Incremental learning to rank with partially-labeled data
Generating unambiguous URL clusters from web search
Topic-specific analysis of search queries
Optimising topical query decomposition
Search shortcuts using click-through data
Query suggestions using query-flow graphs
Using query logs and click data to create improved document descriptions
Intentional query suggestion: making user goals more explicit during search
Usefulness of quality click-through data for training
Comparative analysis of clicks and judgments for IR evaluation
Tailoring click models to user goals

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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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