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Reseach Article

Innovative Predictive Search using Real Time Session based NLP of Search Query

Published on January 2014 by K. Murali Krishna Teja
National Conference on Future Computing 2014
Foundation of Computer Science USA
NCFC2014 - Number 2
January 2014
Authors: K. Murali Krishna Teja
31d1fc05-611c-47e6-a094-ec1eaa19f3eb

K. Murali Krishna Teja . Innovative Predictive Search using Real Time Session based NLP of Search Query. National Conference on Future Computing 2014. NCFC2014, 2 (January 2014), 17-21.

@article{
author = { K. Murali Krishna Teja },
title = { Innovative Predictive Search using Real Time Session based NLP of Search Query },
journal = { National Conference on Future Computing 2014 },
issue_date = { January 2014 },
volume = { NCFC2014 },
number = { 2 },
month = { January },
year = { 2014 },
issn = 0975-8887,
pages = { 17-21 },
numpages = 5,
url = { /proceedings/ncfc2014/number2/14797-1411/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Future Computing 2014
%A K. Murali Krishna Teja
%T Innovative Predictive Search using Real Time Session based NLP of Search Query
%J National Conference on Future Computing 2014
%@ 0975-8887
%V NCFC2014
%N 2
%P 17-21
%D 2014
%I International Journal of Computer Applications
Abstract

Information Retrieval (IR) has come a long way in the recent years with giant strides of research and development in the field. One of the Recent (from 1994), widely renown and hugely used application of IR is a Search Engine. This paper focuses on improvisation of existing predictive search mechanism by using a real time session based NLP of a search query thus resulting in a more diversified but related suggestions being provided to the user. The main intention is to provide appropriate, accurate suggestions to dedicated users of web search engines such as researchers who mostly concentrate on a particular topic to search in a session providing an optimal balance between response rate and accuracy.

References
  1. A. Geetha," A Note on NLP based Search Engines" in International Journal of Wisdom Based Computing, Vol. 1 (2), August 2011.
  2. Zhong, N. , J. Liu and Y. Yao, 2002. "In search of the Wisdom web". IEEE Computer, 35: 27-31.
  3. P. C. Reghu Raj and S. Raman. "Applied Artificial Intelligence", in Taylor and Francis Inc. 19:559-599 2005.
  4. "Information Retrieval and Semantic Web" in Proceedings of the 38th Hawaii International Conference on System Sciences – 2005.
  5. M. Mitra. B. B. Chaudhuri. , "Multilingual IR" in Information Retrieval2, 141-163(2000).
  6. H. Chu. , M. Rosenthal. , "Search engines for WWW: A comparative study and evaluation methodology" 2007.
  7. P. F. Brown, P. V. deSouza, R. L. Mercer, V. J. D. Pietra, and J C. Lai. " Class-based n-gram models of natural language. Computational Linguistics", 18(4):467–479, 1992a.
  8. Cory Barr, Rosie Jones and Moira Regelson,"The Linguistic Structure of English Web-Search Queries"
  9. Hendler, J. , T. B-Lee and E. Miller. "Integrating Applications on the Semantic web". J. Institute Elec. Eng. Japan, 2002. 122: 676-680.
  10. R. K. Ando and T. Zhang. "A framework for learning predictive structures from multiple tasks and unlabeled data". Journal of Machine Learning Research (JMLR), 6:1817–1953, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Session Based Query Processing Natural Language Processing(nlp) intelligent Information Retrieval(iir) Prediction Optimization Semantic Database Session Based Query Processing.