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Comparison and Analysis of Two Approaches to Find Novel Documents out of Several Documents

by Anjali Sharma, Mukesh Rawat
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 2
Year of Publication: 2016
Authors: Anjali Sharma, Mukesh Rawat
10.5120/ijca2016908325

Anjali Sharma, Mukesh Rawat . Comparison and Analysis of Two Approaches to Find Novel Documents out of Several Documents. International Journal of Computer Applications. 136, 2 ( February 2016), 30-34. DOI=10.5120/ijca2016908325

@article{ 10.5120/ijca2016908325,
author = { Anjali Sharma, Mukesh Rawat },
title = { Comparison and Analysis of Two Approaches to Find Novel Documents out of Several Documents },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 2 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number2/24127-2016908325/ },
doi = { 10.5120/ijca2016908325 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:35:58.760916+05:30
%A Anjali Sharma
%A Mukesh Rawat
%T Comparison and Analysis of Two Approaches to Find Novel Documents out of Several Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 2
%P 30-34
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Novelty detection system is used to extract documents with new or novel information from list of documents. Without looking for lot of redundant information, we can get useful information in a limited time. Cosine similarity and Language modeling are the two emerging techniques of information retrieval in today’s scenario. The current study performs the analysis and comparison between these two models.

References
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  2. Manvi Breja, A novel approach for novelty detection of web documents, International Journal of Computer Science and Information Technologies, Vol. 6 (5), 2015, 4257-4262
  3. Ming-Feng Tsai, Ming-Hung Hsu, and Hsin-Hsi Chen, Similarity computation in novelty detection, Department of Computer Science and Information Engineering National Taiwan University, Taiwan.
  4. Djoerd Hiemstra, Language Models, In M. Tamer Özsu and Ling Liu (eds.) Encyclopedia of Database Systems, Springer, ISBN 978-0-387-49616-0, pages 1591-1594, 2009
  5. Jitendra Nath Singh and Sanjay Kumar Dwivedi, A Comparative Study on Approaches of Vector Space Model in Information Retrieval, International Journal of Computer Applications (0975 – 8887) International Conference of Reliability, Infocom Technologies and Optimization, 2013
  6. Stephen Robertson Microsoft Research 7 JJ Thomson Avenue Cambridge CB3 0FB UK Understanding Inverse Document Frequency:On theoretical arguments for IDF, 2004
  7. Xuchang Zou, Raffaella Settimi, Jane Cleland-Huang, Chuan Duan, Thresholding Strategy in Requirements Trace Retrieval
  8. CHENGXIANG ZHAI and JOHN LAFFERTY, Carnegie Mellon University, A Study of Smoothing Methods for Language Models Applied to Information Retrieval, ACM Transactions on Information Systems (TOIS), Volume 22 Issue 2, April 2004  Pages 179-214
  9. Flora S. Tsai · Yi Zhang, Document-to-sentence framework for novelty Detection, Knowl Inf Syst Volume 29 Issue 2, November 2011 Pages419-433
  10. Ian Soboro and Donna Harman National Institute of Standards and Technology Gaithersburg, MD, Novelty Detection: The TREC Experience, HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing Pages 105-112
Index Terms

Computer Science
Information Sciences

Keywords

Cosine similarity Information retrieval Language modeling Novelty detection Smoothing

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