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:1.8081
- @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
Citations