Determining General Term Weighting Schemes for the Vector Space Model of Information Retrieval Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{cummins:2004:AICS,

• author = "Ronan Cummins and Colm O'Riordan",
• title = "Determining General Term Weighting Schemes for the Vector Space Model of Information Retrieval Using Genetic Programming",
• booktitle = "15th Artificial Intelligence and Cognitive Science Conference (AICS 2004)",
• year = "2004",
• editor = "Lorraine McGinty",
• address = "Galway-Mayo Institute of Technology, Castlebar Campus, Ireland",
• month = "8-10 " # sep,
• keywords = "genetic algorithms, genetic programming, NLP",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.99.5031",
• URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsAICS2004.pdf",
• size = "10 pages",
• abstract = "Term weighting schemes play a vital role in the performance of many Information Retrieval models. The vector space model is one such model in which the weights applied to the document terms are of crucial importance to the accuracy of the retrieval system. This paper outlines a procedure using genetic programming to automatically determine term weighting schemes that achieve a high average precision. The schemes are tested on standard test collections and are shown to perform consistently better than the traditional tf-idf weighting schemes. We present an analysis of the evolved weighting schemes to explain their increase in performance. These term weighting schemes are shown to be general across various collections and are shown to adhere to Luhn's theory as both high and low frequency terms are assigned a low weight.",
• notes = "Broken Jan 2013 http://www.gmit.ie/aics_2004/",

}

Genetic Programming entries for Ronan Cummins Colm O'Riordan

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