A Genetic Algorithm For Text Mining
Author(s)
G. Desjardins, R. Godin & R. Proulx
Abstract
Text workers should find ways of representing huge amounts of text in a more
compact form. Textual documents can be represented by concepts. One way to
define the concepts is by the terms, keywords extracted from the textual
documents and cleaned by several processes like stopwords and stemming.
Using the frequencies of the terms, one can quantify the relations between
documents or portions of text. These relations can serve many applications, like
information retrieval or automatic text classification.
Another way to define the concepts is by the sets of correlated terms rather
then by raw terms. Correlated terms usually have a more specific meaning.
Finding meaningful concepts within a huge collection of corpuses in a
reasonable timeframe is a difficult task to accomplish.
This paper describes a new text mining process to uncover interesting term
correlations. The process uses a genetic algorithm to cope with the combinatorial
explosion of the term sets. The genetic algorithm identifies combinations of
terms that optimize an objective function, which is the cornerstone of the
process. We have tested a function designed to optimize the discriminating
power of the term sets.
The genetic model was tested on a TREC sub-collection. The parameters
were set to discover a thousand combinations of correlated terms. These sets of
terms were further added to the basic index and applied to the information
retrieval problem. The experiment revealed that the augmented index was unable
to improve the effectiveness of the retrieval, when compared with the vector
space model.
Keywords: genetic algorithm, co-occurrences, information retrieval, text mining.
Keywords
genetic algorithm, co-occurrences, information retrieval, text mining.
Related Book
Other papers in this volume
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