Abstract
Ranking techniques are effective for finding answers in document collections but the cost of evaluation of ranked queries can be unacceptably high. We propose an evaluation technique that reduces both main memory usage and query evaluation time. based on early recognition of which documents are likely to be highly ranked. Our experiments show that, for our test data, the proposed technique evaluates queries in 20% of the time and 2% of the memory taken by the standard inverted file implementation, without degradation in retrieval effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
C. Buckley and A.F. Lewit. Optimisation of inverted vector searches. In Proc. ACM-SIGIR International Conference on Research and Development in Information Retrieval, pages 97–110, Montreal, Canada, June 1985.
W.B. Frakes and R. Baeza-Yates, editors. Information Retrieval: Data Structures and Algorithms. Prentice-Hall, New Jersey, 1992.
D. Harman and C. Candela. Retrieving records from a gigabyte of text on a minicomputer using statistical ranking. Journal of the American Society for Information Science, 41 (8): 581–589, 1990.
D. Lucarella. A document retrieval system based upon nearest neighbour searching. Journal of Information Science, 14: 25–33, 1988.
A. Moffat and J. Zobel. Parameterised compression for sparse bitmaps. ln Proc. ACM-SIGIR International Conference on Research and Development in Information. Retrieval, pages 274–285, Copenhagen, Denmark, June 1992. ACM Press.
A. Moffat and J. Zobel. Fast ranking in limited space. Technical Report 93/11, Department of Computer Science, The University of Melbourm, 1993. Submitted to the 1994 Data Engineering conference.
National Institute of Standards and Technology. Proc. Text Retrieval Conference (TRAC),Washington, November 1992. Special Publication 500–207.
S.A. Perry and P. Willett. A reniew of the use of inverted files for best matchs searching in information retrieval systems. Journal of Information Science, 6: 59–66, 1983.
M. Persin, J. Zobel, and R. Sacks-Davis. Fast document ranking for large scale information retrieval. Technical Report 94/1, Collaborative Information Technology Research Institute, Department of Computer Science, Royal Melbourne Institute of Technology, Australia, 1994.
G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, MA, 1989.
G. Salton and M.J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1983.
J. Zobel, A. Moffat, and R. Sacks-Davis. An efficient indexing technique for full-text database systems. In Proc. International Conference on Very Large Databases, pages 352–362, Vancouver, Canada, August 1992.
J. Zobel, A. Moffat, and R. Sacks-Davis. Memory-efficient ranking of document collections. Technical Report TR-92–53, Collaborative Information Technology Research Institute, RMIT and The University of Melbourne, Melbourne, Australia, August 1992.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer-Verlag London Limited
About this paper
Cite this paper
Persin, M. (1994). Document Filtering for Fast Ranking. In: Croft, B.W., van Rijsbergen, C.J. (eds) SIGIR ’94. Springer, London. https://doi.org/10.1007/978-1-4471-2099-5_35
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2099-5_35
Publisher Name: Springer, London
Print ISBN: 978-3-540-19889-5
Online ISBN: 978-1-4471-2099-5
eBook Packages: Springer Book Archive