Call for Paper - November 2020 Edition
IJCA solicits original research papers for the November 2020 Edition. Last date of manuscript submission is October 20, 2020. Read More

Query Intent Classification using Semi- Supervised Learning

IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication
© 2014 by IJCA Journal
ETCC - Number 1
Year of Publication: 2014
Safallya Dhar
Sandeepan Swain
B. S. P. Mishra

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

	author = {Safallya Dhar and Sandeepan Swain and B. S. P. Mishra},
	title = {Article: Query Intent Classification using Semi- Supervised Learning},
	journal = {IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication},
	year = {2014},
	volume = {ETCC},
	number = {1},
	pages = {40-43},
	month = {September},
	note = {Full text available}


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.


  • Andrew Brian Goldberg. 2010. New Directions In Semi-Supervised Learning. 24-65.
  • Emily Pitler, Ken Church . 2009. UsingWord-Sense Disambiguation Methods to Classify Web Queries by Intent. 1428-1436.
  • 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.
  • S. Brin, and L. Page. 1998. The Anatomy of a Large Scale Hypertextual Web Search Engine. Computer Network and ISDN Systems (1998), Vol. 30, Issue 1-7, pp. 107-117.
  • Dayong Wu, Yu Zhang, Shiqi Zhao. 2010 Identification of Web Query Intent Based on Query Text and Web Knowledge. First International Conference on Pervasive Computing, Signal Processing and Applications 128-132,
  • Shyh-Jier Huang Ching-Lien Huang. 1996 Improvement of Classification Accuracy by Using Enhanced Query-Based Learning Neural Network. IEEE398-403.
  • Ray-I Chang , Pei-Yung Hsiao. 1997 Unsupervised Query-Based Learning Of Neural Networks Using Selective-Attention And Self-Regulation. IEEE transactions on neural networks, vol. 8, no. 2, 205-218.
  • Madhuri A. Potey, Dhanshri A. Patel, P. K. Sinha. 2012 A survey of Query Log Processing Techniques and evaluation of Web Query Intent Identification. IEEE 1330-1336.
  • Arjit De, S. K. Kopparapu. 2012 . A Rule Based Short Query Intent Identification System, TCS-Innovation Lab. IEEE 212-217.
  • Azin Ashkan, Charles L. A. Clarke, Eugene Agichetin, Qi Guo. 2008. Classifing and Characterizing Query Intent. 456-461.
  • Cristina Gonalez Caro, Ricardo Baeza Yales. 2006 A Multifaceted Approach to Query Intent Classification. (2006) 122-124.
  • David J. Brenes, Daniel Gayo-Avello, Kilian Pérez-González. 2009 Survey and evaluation of query intent detection methods,pp557-564.
  • Raji Sukumar. A, Sarin sukumar A. 2010. Key-Word Based Query Recognition In a Speech Corpus By Using Artificial Neural Networks ,pp 212-217.