Learning in a pairwise term-term proximity framework for information retrieval

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@InProceedings{Cummins:2009:SIGIR,
  author =       "Ronan Cummins and Colm O'Riordan",
  title =        "Learning in a pairwise term-term proximity framework
                 for information retrieval",
  booktitle =    "SIGIR '09: Proceedings of the 32nd international ACM
                 SIGIR conference on Research and development in
                 information retrieval",
  year =         "2009",
  editor =       "James Allan and Javed Aslam",
  pages =        "251--258",
  address =      "Boston, MA, USA",
  publisher_address = "New York, NY, USA",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, information
                 retrieval, learning to rank, proximity",
  isbn13 =       "978-1-60558-483-6",
  DOI =          "doi:10.1145/1571941.1571986",
  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.",
  notes =        "Also known as \cite{1571986}",
}

Genetic Programming entries for Ronan Cummins Colm O'Riordan

Citations