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Performance Assessment using Text Mining

by Radha Shakarmani, Nikhil Kedar, Naman Khandelwal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 12
Year of Publication: 2010
Authors: Radha Shakarmani, Nikhil Kedar, Naman Khandelwal
10.5120/271-431

Radha Shakarmani, Nikhil Kedar, Naman Khandelwal . Performance Assessment using Text Mining. International Journal of Computer Applications. 1, 12 ( February 2010), 1-6. DOI=10.5120/271-431

@article{ 10.5120/271-431,
author = { Radha Shakarmani, Nikhil Kedar, Naman Khandelwal },
title = { Performance Assessment using Text Mining },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 12 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number12/271-431/ },
doi = { 10.5120/271-431 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:46:10.807474+05:30
%A Radha Shakarmani
%A Nikhil Kedar
%A Naman Khandelwal
%T Performance Assessment using Text Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 12
%P 1-6
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Existing search engines have many remarkable capabilities; but what is not among them is deduction capability—the capability to synthesize an answer to a query from bodies of information which reside in various parts of the World Wide Web. Web Intelligence is an area of research which attempts to provide this capability.

References
  1. GATE . www.gate.ac.uk. General Architecture for Text Engineering or GATE is a Java software toolkit originally developed at the University of Sheffield since 1995.
  2. Web Intelligence: kis.maebashi-it.ac.jp/wi01/ www.web-intelligence.com/
  3. Muslea, I. (Ed.). (2004). Papers from the AAAI-2004 Workshop on Adaptive Text Extraction and Mining (ATEM-2004) Workshop, San Jose, CA. AAAI Press.
  4. Weiguo Fan, et. al., “Tapping the Power of Text Mining,” Communications of the ACM, 49(9), 2006.
  5. Tan, A.-H. (1999), “Text Mining: The state of the art and the challenges”, in Proceedings, PAKDD’99 workshop on Knowledge Discovery from Advanced Databases, Beijing, April, 1999.
  6. Intelligence on the Web: www.fas.org/irp/intelwww.html WIN: home WEB INTELLIGENCE NETWORK, smarter.net/
  7. J. Srivastava et al., “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data,” SIGKDD Explorations, vol. 1, no. 2, 2000, pp. 12
  8. Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996), “From data mining to knowledge discovery: An overview”, in U. Fayyad et al. (eds.) Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, Mass.
  9. Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996), “From data mining to knowledge discovery: An overview”, in U. Fayyad et al. (eds.) Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, Mass., 1
  10. IEEE 2000b. IEEE Standard for Modelling and Simulation (M&S) High Level Architecture (HLA) –Federate Interface Specification. IEEE Std 1516.1-2000. IEEE Computer Society, New York, NY.
Index Terms

Computer Science
Information Sciences

Keywords

Web Intelligence NLP Text Mining Information Extraction Information Retrieval GATE GATE

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