Evolving General Term-Weighting Schemes for Information Retrieval: Tests on Larger Collections
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Cummins:2005:AIR,
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author = "Ronan Cummins and Colm O'Riordan",
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title = "Evolving General Term-Weighting Schemes for
Information Retrieval: Tests on Larger Collections",
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journal = "Artificial Intelligence Review",
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year = "2005",
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volume = "24",
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number = "3-4",
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pages = "277--299",
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month = nov,
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email = "ronan.cummins@nuigalway.ie",
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keywords = "genetic algorithms, genetic programming,
term-weighting schemes, Information Retrieval",
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ISSN = "0269-2821",
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DOI = "doi:10.1007/s10462-005-9001-y",
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abstract = "Term-weighting schemes are vital to the performance of
Information Retrieval models that use term frequency
characteristics to determine the relevance of a
document. The vector space model is one such model in
which the weights assigned to the document terms are of
crucial importance to the accuracy of the retrieval
system. We describe a genetic programming framework
used to automatically determine term-weighting schemes
that achieve a high average precision. These schemes
are tested on standard test collections and are shown
to perform as well as, and often better than, the
modern BM25 weighting scheme. We present an analysis of
the schemes evolved to explain the increase in
performance. Furthermore, we show that the global
(collection wide) part of the evolved weighting schemes
also increases average precision over idf on larger
TREC data. These global weighting schemes are shown to
adhere to Luhn's resolving power as middle frequency
terms are assigned the highest weight. However, the
complete weighting schemes evolved on small collections
do not perform as well on large collections. We
conclude that in order to evolve improved local
(within-document) weighting schemes it is necessary to
evolve these on large collections.",
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notes = "www.kluweronline.com/issn/0269-2821",
- }
Genetic Programming entries for
Ronan Cummins
Colm O'Riordan
Citations