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Ranking Strategy Using Hybrid Model

by Pooja Arora, Om Vikas
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 10
Year of Publication: 2010
Authors: Pooja Arora, Om Vikas
10.5120/950-1327

Pooja Arora, Om Vikas . Ranking Strategy Using Hybrid Model. International Journal of Computer Applications. 5, 10 ( August 2010), 10-15. DOI=10.5120/950-1327

@article{ 10.5120/950-1327,
author = { Pooja Arora, Om Vikas },
title = { Ranking Strategy Using Hybrid Model },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 5 },
number = { 10 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume5/number10/950-1327/ },
doi = { 10.5120/950-1327 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:53:53.464206+05:30
%A Pooja Arora
%A Om Vikas
%T Ranking Strategy Using Hybrid Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 5
%N 10
%P 10-15
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Various information retrieval models generate different ranking list as output. This paper presents the comparative analysis of the vector space model and the probabilistic model. Effect of stopword removal is also discussed. A new hybrid model is introduced that combines the Vector Space Model and the Probabilistic model. The resultant model gives better performance. For experiments, we have constructed English-Hindi IR test collection from EMILLE parallel corpus. Relational (stop) words are considered for improving the search results. F-measure and AIP (Average Interpolated Precision) are used for evaluation.

References
  1. G. Salton, A. Wong and S.S. Yang 1975. A vector space model for automatic indexing, communications of the ACM, 18, pages 613-620.
  2. L. Gravano, H. Garcia-Molina.1997. Merging Ranks from Heterogeneous Internet Sources. Very Large Databases (VLDB).
  3. Ashwani Mujoo, Manoj Kumar Malviya, Rajat Moona, T.V. Prabhakar.2000. A Search Engine for Indian Languages, In Proceedings of the First International Conference on Electronic Commerce and Web Technologies, pages 349-358.
  4. Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze.2008.Introduction to Information Retrieval, Cambridge University Press.
  5. S.E. Robertson, K. Sparck Jones.1976. Relevance Weighting of Search Terms, Journal of the American Society for Information Science, pages 129-146.
  6. The EMILLE Corpus, http://ahds.ac.uk/catalogue/collection.htm?uri=lll-2460-1
  7. Prasad Pingali, Jagadeesh Jagalamudi, Vasudeva Varma.2006. Webkhoj : Indian Language IR from multiple character encodings, In Proceedings of the 15th international conference on World Wide Web , ACM , pp : 801-809.
  8. Amaresh Kumar Pandey , Tanveer J Siddiqui.2008. An Unsupervised Hindi Stemmer with heuristic improvements, In the proceedings of the 2nd workshop on Analytics for noisy unstructured text data, ACM , pp : 99-105.
  9. M.F. Porter.1997. An Algorithm for Suffix Stripping.1997. Morgan Kaufmann Multimedia Information And Systems Series , pp :313-316.
Index Terms

Computer Science
Information Sciences

Keywords

IR models comparison Stopword removal English-Hindi parallel corpus Relational Stopwords Hybrid Model Vector Space Model Probabilistic Model

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