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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References
Goldberg, D. E. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Pub. Co.
Lewis, D. D. (1998): Naïve (Bayes) at Forty: The Independence Assumption in Information Retrieval. In: Proceedings of the 10 th European Conference on Machine Learning ECML’98. Springer Verlag, Berlin Heidelberg New York, pp. 4–15.
McCallum, A. and Nigam, K. (1998): A Comparison of Event Models for Naïve Bayes Text Classi.cation. In: Proceedings of the AAAI-98 Workshop on Learning for Text Categorization. ICML/AAAI-98, Madison, Wisconsin, July 26–27.
Žižka, J., Bourek, A. (2002): Automated Selection of Interesting Medical Text Documents. In: Proceedings of the Fifth International Conference Text, Speech, and Dialogue TSD-2002. Springer Verlag, Berlin Heidelberg New York, LNAI 2448, pp. 99–106.
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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