Abstract
This paper describes some experiments that used Genetic Algorithms (GAs) for looking for important word associations (phrases) in unstructured text documents obtained from the Internet in the area of a specialized medicine. GAs can evolve sets of word associations with assigned significance weights from the document categorization point of view (here two classes: relevant and irrelevant documents). The categorization was similarly reliable like the naïve Bayes method using just individual words; in addition, in this case GAs provided phrases consisting of one, two, or three words. The selected phrases were quite meaningful from the human point of view.
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Žižka, J., Bourek, A. (2002): Automated Selection of Interesting Medical Text Documents by the TEA Text Analyzer. In: A. Gelbukh (Ed.) Computational Linguistics and Intelligent Text Processing, Lecture Notes in Computer Science, N 2276, Springer-Verlag, Berlin, Heidelberg, New York, 2002, pp. 402–404.
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Žižka, J., Šrédl, M., Bourek, A. (2003). Searching for Significant Word Associations in Text Documents Using Genetic Algorithms. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2003. Lecture Notes in Computer Science, vol 2588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36456-0_64
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DOI: https://doi.org/10.1007/3-540-36456-0_64
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