Abstract
This short paper briefly describes the experience in the automated selection of interesting medical text documents by the TEA text analyzer based on the naïve Bayes classifier. Even if the used type of the classifier provides generally good results, physicians needed certain supporting functions to obtain really interesting medical text documents, for example, from resources like the Internet. The influence of the functions is summarized and discussed. In addition, some remaining problems are mentioned.
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Žižka, J., Bourek, A. (2002). Automated Selection of Interesting Medical Text Documents by the TEA Text Analyzer. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2002. Lecture Notes in Computer Science, vol 2276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45715-1_42
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DOI: https://doi.org/10.1007/3-540-45715-1_42
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