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Varying approaches to topical web query classification

Published: 23 July 2007 Publication History

Abstract

Topical classification of web queries has drawn recent interest because of the promise it offers in improving retrieval effectiveness and efficiency. However, much of this promise depends on whether classification is performed before or after the query is used to retrieve documents. We examine two previously unaddressed issues in query classification: pre versus post-retrieval classification effectiveness and the effect of training explicitly from classified queries versus bridging a classifier trained using a document taxonomy. Bridging classifiers map the categories of a document taxonomy onto those of a query classification problem to provide sufficient training data. We find that training classifiers explicitly from manually classified queries outperforms the bridged classifier by 48% in F1 score. Also, a pre-retrieval classifier using only the query terms performs merely 11% worse than the bridged classifier which requires snippets from retrieved documents.

References

[1]
Beitzel, S.M., Jensen, E.C., Lewis, D.D., Chowdhury, A., Kolcz, A. and Frieder, O., Improving Automatic Query Classification via Semi-supervised Learning. in IEEE ICDM, 2005, 42--49.
[2]
Li, Y., Zheng, Z. and Dai, H.K. KDD Cup-2005 Report: Facing a Great Challenge. SIGKDD Explorations, 7 (2), 2005, 91--99.
[3]
Shen, D., Sun, J., Yang, Q. and Chen, Z., Building bridges for web query classification. in ACM SIGIR, 2006, 131--138.

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      cover image ACM Conferences
      SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
      July 2007
      946 pages
      ISBN:9781595935977
      DOI:10.1145/1277741
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 23 July 2007

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      Author Tags

      1. query classification
      2. web search

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      SIGIR07
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      SIGIR07: The 30th Annual International SIGIR Conference
      July 23 - 27, 2007
      Amsterdam, The Netherlands

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      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      • (2020)Query ClassificationQuery Understanding for Search Engines10.1007/978-3-030-58334-7_2(15-41)Online publication date: 2-Dec-2020
      • (2019)A hybrid deep neural network model for query intent classificationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18268236:6(6413-6423)Online publication date: 1-Jan-2019
      • (2019)You Are What You Search: Attribute Inference Attacks Through Web Search QueriesSecurity with Intelligent Computing and Big-data Services10.1007/978-3-030-16946-6_27(343-358)Online publication date: 17-Apr-2019
      • (2018)Multi-Task Learning for Email Search Ranking with Auxiliary Query ClusteringProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3272019(2127-2135)Online publication date: 17-Oct-2018
      • (2017)How questions are posed to a search engine? An empiricial analysis of question queries in a large scale Persian search engine log2017 3th International Conference on Web Research (ICWR)10.1109/ICWR.2017.7959310(84-89)Online publication date: Apr-2017
      • (2016)Recommendation engine feedback session strategy for mapping user search goals (FFS: Recommendation system)2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)10.1109/ICEEOT.2016.7755581(4572-4580)Online publication date: Mar-2016
      • (2015)Prediction of User Interests for Providing Relevant Information Using Relevance Feedback and Re-rankingInternational Journal of Intelligent Information Technologies10.4018/IJIIT.201510010411:4(55-71)Online publication date: 1-Oct-2015
      • (2015)Solution for queries for top-k relevant attribute2015 International Conference on Communications and Signal Processing (ICCSP)10.1109/ICCSP.2015.7322770(1520-1524)Online publication date: Apr-2015
      • (2015)An Integrating Approach by Using FP Tree and Genetic Algorithm for Inferring User Interesting ResultsProceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 201410.1007/978-3-319-11933-5_6(43-50)Online publication date: 2015
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