Learning in a pairwise term-term proximity framework for information retrieval
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Cummins:2009:SIGIR,
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author = "Ronan Cummins and Colm O'Riordan",
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title = "Learning in a pairwise term-term proximity framework
for information retrieval",
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booktitle = "SIGIR '09: Proceedings of the 32nd international ACM
SIGIR conference on Research and development in
information retrieval",
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year = "2009",
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editor = "James Allan and Javed Aslam",
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pages = "251--258",
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address = "Boston, MA, USA",
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publisher_address = "New York, NY, USA",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, information
retrieval, learning to rank, proximity",
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isbn13 = "978-1-60558-483-6",
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DOI = "doi:10.1145/1571941.1571986",
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abstract = "Traditional ad hoc retrieval models do not take into
account the closeness or proximity of terms. Document
scores in these models are primarily based on the
occurrences or non-occurrences of query-terms
considered independently of each other. Intuitively,
documents in which query-terms occur closer together
should be ranked higher than documents in which the
query-terms appear far apart.
This paper outlines several term-term proximity
measures and develops an intuitive framework in which
they can be used to fully model the proximity of all
query-terms for a particular topic. As useful proximity
functions may be constructed from many proximity
measures, we use a learning approach to combine
proximity measures to develop a useful proximity
function in the framework. An evaluation of the best
proximity functions show that there is a significant
improvement over the baseline ad hoc retrieval model
and over other more recent methods that employ the use
of single proximity measures.",
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notes = "Also known as \cite{1571986}",
- }
Genetic Programming entries for
Ronan Cummins
Colm O'Riordan
Citations