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Combining Information Extraction with Genetic Algorithms for Text Mining
May/June 2004 (vol. 19 no. 3)
pp. 22-30
John Atkinson-Abutridy, University of Edinburgh
Chris Mellish, University of Edinburgh
Stuart Aitken, University of Edinburgh
Text mining discovers unseen patterns in textual databases. But these discoveries are useless unless they contribute valuable knowledge for users who make strategic decisions. Confronting this issue can lead to a complicated activity called knowledge discovery from texts, which deals with both discovering unseen knowledge and evaluating this potentially valuable knowledge. KDT can benefit from techniques that have been useful in data mining or knowledge discovery from databases. However, we can't immediately apply data mining techniques to text data for text mining because they assume a structure in the source data that isn't in free text. We must therefore use new representations for text data. An evolutionary approach that combines information extraction technology and genetic algorithms can produce a new, integrated hypothesis for text mining.
Index Terms:
text mining, knowledge discovery from texts, semantic analysis, genetic algorithms, multiobjective optimization
John Atkinson-Abutridy, Chris Mellish, Stuart Aitken, "Combining Information Extraction with Genetic Algorithms for Text Mining," IEEE Intelligent Systems, vol. 19, no. 3, pp. 22-30, May-June 2004, doi:10.1109/MIS.2004.4
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