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@inproceedings{490363,
author = {Žižka, Jan and Šrédl, Michal and Bourek, Aleš},
address = {Berlin Heidelberg New York},
booktitle = {Computional Linguistics and Intelligent Text Processing},
keywords = {machine learning; text document processing; genetic algorithms; naive Bayes method},
language = {eng},
location = {Berlin Heidelberg New York},
isbn = {3-540-00532-3},
pages = {584-587},
publisher = {Springer Verlag},
title = {Searching for Significant Word Associations in Text Documents Using Genetic Algorithms},
year = {2003}
}
TY - JOUR
ID - 490363
AU - Žižka, Jan - Šrédl, Michal - Bourek, Aleš
PY - 2003
TI - Searching for Significant Word Associations in Text Documents Using Genetic Algorithms
PB - Springer Verlag
CY - Berlin Heidelberg New York
SN - 3540005323
KW - machine learning
KW - text document processing
KW - genetic algorithms
KW - naive Bayes method
N2 - The paper describes experiments that used Genetic Algorithms for looking for important word assocoations (phrases) in unstructured text documents obtained from the Internet in the area of a specialized medicine branch. Genetic alforithms can evolve sets of word associations with assigned significance weights from the document categorization point of view (relevant and irrelevant documents). The categorization is similarly reliable like the naive Bayes classification based on individual words. In addition, genetic algorithms provided phrases consisting of one, two, and three words. The phrases were quite meaningful from the human point of view.
ER -
ŽIŽKA, Jan, Michal ŠRÉDL and Aleš BOUREK. Searching for Significant Word Associations in Text Documents Using Genetic Algorithms. In \textit{Computional Linguistics and Intelligent Text Processing}. Berlin Heidelberg New York: Springer Verlag, 2003, p.~584-587. ISBN~3-540-00532-3.
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