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Query Intent Classification using Semi- Supervised Learning

Published on September 2014 by Safallya Dhar, Sandeepan Swain, B. S. P. Mishra
journal_cover_thumbnail

International Conference on Emergent Trends in Computing and Communication
Foundation of Computer Science USA
ETCC - Number 1
September 2014
Authors: Safallya Dhar, Sandeepan Swain, B. S. P. Mishra
72d29b1b-c091-47f9-ad46-1a442704ca01

Safallya Dhar, Sandeepan Swain, B. S. P. Mishra . Query Intent Classification using Semi- Supervised Learning. International Conference on Emergent Trends in Computing and Communication. ETCC, 1 (September 2014), 40-43.

@article{
author = { Safallya Dhar, Sandeepan Swain, B. S. P. Mishra },
title = { Query Intent Classification using Semi- Supervised Learning },
journal = { International Conference on Emergent Trends in Computing and Communication },
issue_date = { September 2014 },
volume = { ETCC },
number = { 1 },
month = { September },
year = { 2014 },
issn = 0975-8887,
pages = { 40-43 },
numpages = 4,
url = { /proceedings/etcc/number1/17646-1418/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emergent Trends in Computing and Communication
%A Safallya Dhar
%A Sandeepan Swain
%A B. S. P. Mishra
%T Query Intent Classification using Semi- Supervised Learning
%J International Conference on Emergent Trends in Computing and Communication
%@ 0975-8887
%V ETCC
%N 1
%P 40-43
%D 2014
%I International Journal of Computer Applications
Abstract

The Query Intent classification using semi-supervised learning about ti find a better away to search the web precision that result surfer want to search is 99. 8% matched, but due to search engine know what type of query user want to search and logs that are residing in the server of search engine . Which are put in data warehouse of vendor search engine for testing purpose that what type was given. In this paper algorithm is proposed how to increase the precision rate.

References
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  2. Emily Pitler, Ken Church . 2009. UsingWord-Sense Disambiguation Methods to Classify Web Queries by Intent. 1428-1436.
  3. Jian Hu, Gang Wang, Fred Lochovsky, Jian-Tao Sun, Zheng Chen. :2009 Understanding User's Query Intent with Wikipedia. International World Wide Web Conference Committee (IW3C2) 123-142.
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  9. Arjit De, S. K. Kopparapu. 2012 . A Rule Based Short Query Intent Identification System, TCS-Innovation Lab. IEEE 212-217.
  10. Azin Ashkan, Charles L. A. Clarke, Eugene Agichetin, Qi Guo. 2008. Classifing and Characterizing Query Intent. 456-461.
  11. Cristina Gonalez Caro, Ricardo Baeza Yales. 2006 A Multifaceted Approach to Query Intent Classification. (2006) 122-124.
  12. David J. Brenes, Daniel Gayo-Avello, Kilian Pérez-González. 2009 Survey and evaluation of query intent detection methods,pp557-564.
  13. Raji Sukumar. A, Sarin sukumar A. 2010. Key-Word Based Query Recognition In a Speech Corpus By Using Artificial Neural Networks ,pp 212-217.
Index Terms

Computer Science
Information Sciences

Keywords

Query Intent Classification Semi-supervised Tagging Tokens