Evolving local and global weighting schemes in information retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Cummins:2006:IR,
  author =       "Ronan Cummins and Colm O'Riordan",
  title =        "Evolving local and global weighting schemes in
                 information retrieval",
  journal =      "Information Retrieval",
  year =         "2006",
  volume =       "9",
  number =       "3",
  pages =        "311--330",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Information
                 Retrieval, Term-Weighting Schemes",
  ISSN =         "1386-4564",
  DOI =          "doi:10.1007/s10791-006-1682-6",
  abstract =     "This paper describes a method, using Genetic
                 Programming, to automatically determine term weighting
                 schemes for the vector space model. Based on a set of
                 queries and their human determined relevant documents,
                 weighting schemes are evolved which achieve a high
                 average precision. In Information Retrieval (IR)
                 systems, useful information for term weighting schemes
                 is available from the query, individual documents and
                 the collection as a whole.

                 We evolve term weighting schemes in both local
                 (within-document) and global (collection-wide) domains
                 which interact with each other correctly to achieve a
                 high average precision. These weighting schemes are
                 tested on well-known test collections and are compared
                 to the traditional tf-idf weighting scheme and to the
                 BM25 weighting scheme using standard IR performance
                 metrics. Furthermore, we show that the global weighting
                 schemes evolved on small collections also increase
                 average precision on larger TREC data. These global
                 weighting schemes are shown to adhere to Luhn's
                 resolving power as both high and low frequency terms
                 are assigned low weights. However, the local weightings
                 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.",
}

Genetic Programming entries for Ronan Cummins Colm O'Riordan

Citations