Comparison and Evaluation of Information Retrieval Models

Authors

  • Agha Azeem Rehma Department of Computer Science, University of Management and Technology, Lahore, Pakistan
  • Mazhar Javed Awan Department of Computer Science, University of Management and Technology, Lahore, Pakistan
  • Ilyas Butt Department of Computer Science, University of Management and Technology, Lahore, Pakistan

DOI:

https://doi.org/10.21015/vtse.v13i1.496

Abstract

Recently data is growing day by day in the internet . Data is in the form of  Structured , unstructured  and semi structured in nature. Information Retrieval is the field which is regarding the study of retrieval of unstructured or semi structured documents. For every aspect IR is being used in there are different models being used in them. There are so many models in so many applications,each having some relation to one another.In this paper we will evaluate and compare various IR model techniques and algorithms and see which model excels in which field of application.

References

Salton, G., Fox, E. A., & Wu, H. (1983). Extended Boolean information retrieval. Communications of the ACM, 26(11), 1022-1036. DOI: https://doi.org/10.1145/182.358466

Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4). New Jersey: Prentice hall. DOI: https://doi.org/10.1109/45.468220

Tsatsaronis, G., & Panagiotopoulou, V. (2009, April). A generalized vector space model for text retrieval based on semantic relatedness. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop (pp. 70-78). Association for Computational Linguistics. DOI: https://doi.org/10.3115/1609179.1609188

Raghavan, V. V., & Wong, S. M. (1986). A critical analysis of vector space model for information retrieval. Journal of the American Society for information Science, 37(5), 279-287. DOI: https://doi.org/10.1002/(SICI)1097-4571(198609)37:5<279::AID-ASI1>3.0.CO;2-Q

Hofmann, T. (2017, August). Probabilistic latent semantic indexing. In ACM SIGIR Forum (Vol. 51, No. 2, pp. 211-218). ACM. DOI: https://doi.org/10.1145/3130348.3130370

Grossman, D. A., & Frieder, O. (2012). Information retrieval: Algorithms and heuristics (Vol. 15). Springer Science & Business Media.

Turtle, H., & Croft, W. B. (2017, August). Inference networks for document retrieval. In ACM SIGIR Forum (Vol. 51, No. 2, pp. 124-147). ACM. DOI: https://doi.org/10.1145/3130348.3130361

Ross, S. M. (2014). Introduction to probability models. Academic press. DOI: https://doi.org/10.1016/B978-0-12-407948-9.00001-3

Mogotsi, I. C. (2010). Christopher d. manning, prabhakar raghavan, and hinrich schütze: Introduction to information retrieval. DOI: https://doi.org/10.1007/s10791-009-9115-y

Downloads

Published

2018-03-30

How to Cite

Rehma, A. A., Awan, M. J., & Butt, I. (2018). Comparison and Evaluation of Information Retrieval Models. VFAST Transactions on Software Engineering, 6(1), 7–14. https://doi.org/10.21015/vtse.v13i1.496