Skip to main content
SpringerLink
Log in

Web Queries Classification Based on the Syntactical Patterns of Search Types

  • Conference paper
  • First Online:
Book cover Speech and Computer (SPECOM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10458))

Included in the following conference series:

Abstract

Nowadays, people make frequent use of search engines in order to find the information they need on the web. The abundance of available data has rendered the process of obtaining relevant information challenging in terms of processing and analyzing it. A broad range of web queries classification techniques have been proposed with the aim of helping in understanding the actual intent behind a web search. In this research, we have categorized search queries through introducing Search Type Syntactical Patterns for automatically identifying and classifying search engine user queries. Experiments show that our approach has a good level of accuracy in identifying different search types.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.cs.waikato.ac.nz/ml/weka/.

  2. 2.

    http://www.researchpipeline.com/mediawiki/index.php?title=AOL_Search_Query_Logs.

References

  1. Ashkan, A., Clarke, C.L.A., Agichtein, E., Guo, Q.: Classifying and characterizing query intent. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 578–586. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00958-7_53

    Chapter  Google Scholar 

  2. Baeza-Yates, R., Calderón-Benavides, L., González-Caro, C.: The intention behind web queries. In: Crestani, F., Ferragina, P., Sanderson, M. (eds.) SPIRE 2006. LNCS, vol. 4209, pp. 98–109. Springer, Heidelberg (2006). doi:10.1007/11880561_9

    Chapter  Google Scholar 

  3. Barr, C., Jones, R., Regelson, M.: The linguistic structure of English web-search queries. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1021–1030. Association for Computational Linguistics (2008)

    Google Scholar 

  4. Beitzel, S.M., Jensen, E.C., Frieder, O., Grossman, D., Lewis, D.D., Chowdhury, A., Kolcz, A.: Automatic web query classification using labeled and unlabeled training data. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 581–582. ACM (2005)

    Google Scholar 

  5. Bhatia, S., Brunk, C., Mitra, P.: Analysis and automatic classification of web search queries for diversification requirements. Proc. Am. Soc. Inf. Sci. Technol. 49(1), 1–10 (2012)

    Article  Google Scholar 

  6. Broder, A.: A taxonomy of web search. ACM Sigir Forum 36, 3–10 (2002). ACM

    Article  MATH  Google Scholar 

  7. Calderón-Benavides, L., González-Caro, C., Baeza-Yates, R.: Towards a deeper understanding of the users query intent. In: SIGIR 2010 Workshop on Query Representation and Understanding, pp. 21–24 (2010)

    Google Scholar 

  8. Figueroa, A.: Exploring effective features for recognizing the user intent behind web queries. Comput. Ind. 68, 162–169 (2015)

    Article  Google Scholar 

  9. Hernández, I., Gupta, P., Rosso, P., Rocha, M.: A simple model for classifying web queries by user intent. In: 2nd Spanish Conference on Information Retrieval, CERI 2012, pp. 235–240 (2012)

    Google Scholar 

  10. Højgaard, C., Sejr, J., Cheong, Y.G.: Query categorization from web search logs using machine learning algorithms. Int. J. Database Theory Appl. 9(9), 139–148 (2016)

    Article  Google Scholar 

  11. Jansen, B.J., Booth, D.L., Spink, A.: Determining the informational, navigational, and transactional intent of web queries. Inf. Process. Manag. 44(3), 1251–1266 (2008)

    Article  Google Scholar 

  12. Kanavos, A., Theodoridis, E., Tsakalidis, A.K.: Extracting knowledge from web search engine results. In: IEEE 24th International Conference on Tools with Artificial Intelligence, pp. 860–867 (2012)

    Google Scholar 

  13. Kathuria, A., Jansen, B.J., Hafernik, C., Spink, A.: Classifying the user intent of web queries using k-means clustering. Internet Res. 20(5), 563–581 (2010)

    Article  Google Scholar 

  14. Kellar, M., Watters, C., Shepherd, M.: A goal-based classification of web information tasks. Proc. Am. Soc. Inf. Sci. Technol. 43(1), 1–22 (2006)

    Google Scholar 

  15. Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in web search. In: Proceedings of the 14th International Conference on World Wide Web, pp. 391–400. ACM (2005)

    Google Scholar 

  16. Lewandowski, D., Drechsler, J., Mach, S.: Deriving query intents from web search engine queries. J. Am. Soc. Inform. Sci. Technol. 63(9), 1773–1788 (2012)

    Article  Google Scholar 

  17. Li, X.: Understanding the semantic structure of noun phrase queries. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1337–1345. Association for Computational Linguistics (2010)

    Google Scholar 

  18. Liu, Y., Zhang, M., Ru, L., Ma, S.: Automatic query type identification based on click through information. In: Ng, H.T., Leong, M.-K., Kan, M.-Y., Ji, D. (eds.) AIRS 2006. LNCS, vol. 4182, pp. 593–600. Springer, Heidelberg (2006). doi:10.1007/11880592_51

    Chapter  Google Scholar 

  19. Mendoza, M., Zamora, J.: Identifying the intent of a user query using support vector machines. In: Karlgren, J., Tarhio, J., Hyyrö, H. (eds.) SPIRE 2009. LNCS, vol. 5721, pp. 131–142. Springer, Heidelberg (2009). doi:10.1007/978-3-642-03784-9_13

    Chapter  Google Scholar 

  20. Mitchell, T.M.: Machine Learning. McGraw hill (1997)

    Google Scholar 

  21. Mohasseb, A., El-Sayed, M., Mahar, K.: Automated identification of web queries using search type patterns. In: WEBIST (2). pp. 295–304 (2014)

    Google Scholar 

  22. Morrison, J.B., Pirolli, P., Card, S.K.: A taxonomic analysis of what world wide web activities significantly impact people’s decisions and actions. In: CHI 2001 Extended Abstracts on Human Factors in Computing Systems, pp. 163–164. ACM (2001)

    Google Scholar 

  23. Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: InfoScale, vol. 152, p. 1 (2006)

    Google Scholar 

  24. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  25. Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, San Francisco (2014)

    Google Scholar 

  26. Rennie, J.D., Shih, L., Teevan, J., Karger, D.R., et al.: Tackling the poor assumptions of naive bayes text classifiers. In: ICML, Washington DC, vol. 3, pp. 616–623 (2003)

    Google Scholar 

  27. Rose, D.E., Levinson, D.: Understanding user goals in web search. In: Proceedings of the 13th International Conference on World Wide Web, pp. 13–19. ACM (2004)

    Google Scholar 

  28. Saha Roy, R.: Analyzing linguistic structure of web search queries. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 395–400. ACM (2013)

    Google Scholar 

  29. Song, R., Dou, Z., Hon, H.W., Yu, Y.: Learning query ambiguity models by using search logs. J. Comput. Sci. Technol. 25(4), 728–738 (2010)

    Article  MathSciNet  Google Scholar 

  30. Wu, D., Zhang, Y., Zhao, S., Liu, T.: Identification of web query intent based on query text and web knowledge. In: 2010 First International Conference on Pervasive Computing Signal Processing and Applications (PCSPA), pp. 128–131. IEEE (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Alaa Mohasseb, Mohamed Bader-El-Den & Mihaela Cocea

  2. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Andreas Kanavos

Authors
  1. Alaa Mohasseb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Bader-El-Den
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Kanavos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mihaela Cocea
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alaa Mohasseb .

Editor information

Editors and Affiliations

  1. SPIIRAS, Saint Petersburg, Russia

    Alexey Karpov

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

  3. University of Hertfordshire, Hatfield, United Kingdom

    Iosif Mporas

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Mohasseb, A., Bader-El-Den, M., Kanavos, A., Cocea, M. (2017). Web Queries Classification Based on the Syntactical Patterns of Search Types. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_81

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66429-3_81

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66428-6

  • Online ISBN: 978-3-319-66429-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation