The week's pick
Random Articles
Reseach Article
Article:Language Model for Information Retrieval
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 7 |
| Year of Publication: 2010 |
| Authors: Pritam Singh Negi, M.M.S. Rauthan, H.S. Dhami |
10.5120/1692-2197
|
Pritam Singh Negi, M.M.S. Rauthan, H.S. Dhami . Article:Language Model for Information Retrieval. International Journal of Computer Applications. 12, 7 ( December 2010), 13-17. DOI=10.5120/1692-2197
Abstract
In the present work an attempt has been made to discuss the applicability of language model as an approach to calculate the relevance of the document by utilizing user-supplied information of those documents that are relevant to the query items. This method shall have the advantage of improving retrieval performance as we have utilized user-supplied information of those documents that are relevant to the query in question. The design and implementation of information retrieval systems is concerned with methods for storing, organizing and retrieving information from a collection of documents. The quality of a system is measured by how useful it is to the typical users of the system. In this approach, a query shall be considered generated from an “ideal” document that shall satisfy the information need. The system’s job has been to calculate the frequency of the word in the given document and rank them accordingly.
References
- Bodoff, D., & Robertson, S. E. (2004). New unified probabilistic model. Journal of the American Society for Information Science and Technology, 55(6), 471–487.
- Bong-Hyun Cho, Changki Lee and Gary Geunbae Lee (2003) Exploring term dependences in probabilistic information retrieval model, Information processing and Management, 39(4), pp.505-519.
- C.D. Manning, P. Raghavan, H. Schütze (2008) Classical and web information retrieval systems: algorithms, mathematical foundations and practical issues in .Introduction to information retrieval, Cambridge.
- Croft, W. B., & Lafferty, J. (Eds.). (2003). Language modeling for information retrieval. Boston: Kluwer Academic.
- G.Salton, A. Wong, and C. S. Yang (1975), "A Vector Space Model for Automatic Indexing," Communications of the ACM, vol. 18, nr. 11, pages 613–620.
- Jun Wang and Jianhan Jhu (2010) On statistical analysis and optimization of Information retrieval effectiveness Metrics, In Proceedings of the 33rd International ACM SIGIR conference on Research and Development in Information Retrieval edited by Hsin-His Chen, Efthismis N.Efthimiadis, Jacques Savoy, Fabio Crestani Lugano and Stephane Marehand-Maillet, Association for Computing Machinery, New York. pp. 226-233.
- K. Sparck Jones, S. Walker and S.E. Robertson (2000) A probabilistic model of information retrieval: development and comparative experiments. Information Processing and Management 36, Part 1 779-808.
- Liqi Gao, Yu Zhang, Ting Liu & Guiping Liu (2006) Word sense Language model for Information retrieval, Lecture notes in Computer Science, Volume 4182/2006, 158-171.
- Maron, M. E., & Kuhns, J. (1960). On relevance, probabilistic indexing and information retrieval. Journal of the Association for Computing Machinery, 7(3), 216–244.
- Nick Craswell, Stephen Robertson, Hugo Zaragoza, and Michael Taylor. Relevance weighting for query independent evidence. In Proceedings of ACM SIGIR’2005, Salvador, Brazil, 2005.
- Victor P. Lavrenko (2010) Introduction to probabilistic models in Information retrieval. In proceedings of the 33rd International ACM SIGIR conference on Research and Development in Information Retrieval edited by Hsin-His Chen, Efthismis N.Efthimiadis, Jacques Savoy, Fabio Crestani Lugano and Stephane Marehand-Maillet, Association for Computing Machinery, New York. pp. 905.
- Zhai C. and Lafferty J. (2006) A risk minimization framework for information retrieval. Information processing and Management, 42(1), 31-55.
Index Terms
Keywords