Performance Analysis of Information Retrieval Models on Word Pair Index Structure

  • N. Karthika
  • B. Janet
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)


This paper analyzes the performance of word pair index structure for various information retrieval models. Word pair index structure is the most efficient for solving contextual queries and it is a precision-enhancing structure. The selection of information retrieval model is very important as it precisely influences the outcome of information retrieval system. This paper analyzes the performance of different information retrieval models using word pair index structure. It is found that there is an increase in precision of 18% when compared with traditional inverted index structure, and recall is 8% in the inverted word pair index structure.The mean average precision is increased by 26%, and R-precision is increased by 20%.


  1. 1.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval. In: McGraw-Hill Computer Science Series. McGraw-Hill (1983)Google Scholar
  2. 2.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA (2008)Google Scholar
  3. 3.
    Porter, M.F.: Readings in information retrieval. In: Chapter An Algorithm for Suffix Stripping, pp. 313–316. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1997)Google Scholar
  4. 4.
    Karthika, N., Janet, B.: Word pair index structure for information retrieval using terrier 3.5. In: IEEE Technically Sponsored International Conference on Computational Intelligence on Data Science (ICCIDS) June, 2017 (In Press)Google Scholar
  5. 5.
    Amati, Gianni, Rijsbergen, Cornelis Joost Van: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. (TOIS) 20(4), 357–389 (2002)CrossRefGoogle Scholar
  6. 6.
    Maron, M.E., Kuhns, J.L.: On relevance, probabilistic indexing and information retrieval. J. ACM (JACM), 7(3), 216–244 (1960)CrossRefGoogle Scholar
  7. 7.
    Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. J. Assoc. Inf. Sci. Technol. 27(3), 129–146 (1976)CrossRefGoogle Scholar
  8. 8.
    Harter, S.P.: A probabilistic approach to automatic keyword indexing. Part ii. An algorithm for probabilistic indexing. J. Assoc. Inf. Sci. Technol. 26(5), 280–289 (1975)CrossRefGoogle Scholar
  9. 9.
    Robertson, S.E., Walker, S.: Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 232–241. Springer, New York, Inc. (1994)CrossRefGoogle Scholar
  10. 10.
    Dong, H., Hussain, F.K., Chang, E.: A survey in traditional information retrieval models. In: 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, DEST 2008, pp. 397–402. IEEE (2008)Google Scholar
  11. 11.
    Ruban, S., Sam, S.B., Serrao, L.V., Harshitha.: A Study and Analysis of Information Retrieval Models, pp. 230–236 (2015)Google Scholar
  12. 12.
    Jiang, H.: Study on the performance measure of information retrieval models. In: 2009 International Symposium on Intelligent Ubiquitous Computing and Education, pp. 436–439. IEEE (2009)Google Scholar
  13. 13.
    Zobel, Justin, Moffat, Alistair, Ramamohanarao, Kotagiri: Inverted files versus signature files for text indexing. ACM Trans. Database Syst. (TODS) 23(4), 453–490 (1998)CrossRefGoogle Scholar
  14. 14.
    Mitra, Mandar, Chaudhuri, B.B.: Information retrieval from documents: a survey. Inf. Retr. 2(2–3), 141–163 (2000)CrossRefGoogle Scholar
  15. 15.
    Janet, B., Reddy, A.V.: Wordpair index: a nextword index structure for phrase retrieval. Int. J. Recent Trends Eng. Technol. 3(2) (2010)Google Scholar
  16. 16.
    Bahle, D., Williams, H.E., Zobel. J.: Efficient phrase querying with an auxiliary index. In: Proceedings of the 25th Annual International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 215–221. ACM (2002)Google Scholar
  17. 17.
    Paik, J.H.: A novel tf-idf weighting scheme for effective ranking. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 343–352. ACM (2013)Google Scholar
  18. 18.
    Gasmi, K., Khemakhem, M.T., Jemaa, M.B.: Word indexing versus conceptual indexing in medical image retrieval. In: CLEF (Online Working Notes/Labs/Workshop) (2012)Google Scholar
  19. 19.
    Singh, A., Dey, N., Ashour, A.S., Santhi, V.: Web Semantics for Textual and Visual Information Retrieval, 01 (2017)Google Scholar
  20. 20.
  21. 21.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ApplicationsNational Institute of TechnologyTiruchirappalliIndia

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