| Query side evaluation: an empirical analysis of effectiveness and effort |
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Annual ACM Conference on Research and Development in Information Retrieval
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Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
table of contents
Boston, MA, USA
SESSION: Query formulation
table of contents
Pages 556-563
Year of Publication: 2009
ISBN:978-1-60558-483-6
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ABSTRACT
Typically, Information Retrieval evaluation focuses on measuring the performance of the system's ability at retrieving relevant information, and not the query's ability. However, the effectiveness of a retrieval system is strongly influenced by the quality of the query submitted. In this paper, the effectiveness and effort of querying is empirically examined in the context of the Principle of Least Effort, Zipf's Law and the Law of Diminishing Returns. This query focused investigation leads to a number of novel findings which should prove useful in the development of future retrieval methods and evaluation techniques. While, also motivating further research into query side evaluation.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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G.K. Zipf. Human Behavior and the Principle of Least-Effort. Addison-Wesley, 1949.
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C.R.S.A. Clauset and M.E.J. Newman. Power-law distributions in empirical data. URL http://arxiv.org/abs/0706.1062v1, 2007.
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4
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5
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6
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R.K. Belew. Finding Out About. Cambridge Univ. Press, 2000.
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7
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8
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9
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Chris Buckley , Darrin Dimmick , Ian Soboroff , Ellen Voorhees, Bias and the limits of pooling, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA
[doi> 10.1145/1148170.1148284]
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12
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13
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15
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A.J. Lotka. The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12):317--324, 1926.
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David R. H. Miller , Tim Leek , Richard M. Schwartz, A hidden Markov model information retrieval system, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.214-221, August 15-19, 1999, Berkeley, California, United States
[doi> 10.1145/312624.312680]
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18
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19
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Y. Zhao, F. Scholer, and Y. Tsegay. Effective pre-retrieval query performance prediction using similarity and variability evidence. In ECIR 08: Proceedings of the European Conference in Information Retrieval, pages 52--64, 2008.
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