Abstract
This paper describes results achieved with a text-document classification tool TEA (TExt Analyzer) based on the naïve Bayes algorithm. TEA provides also a set of additional functions, which can assist users at fine-tuning the text classifiers and improving the classification accuracy, mainly through modifications of dictionaries generated during the training phase. Experiments, described in the paper, aimed at supporting work with medical unstructured text documents downloaded from the Internet. Good and stable results (around 97% of the classification accuracy) were achieved for selecting documents in a certain area of interest among a large number of documents from different areas.
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Žižka, J., Bourek, A., Frey, L. (2000). TEA: A Text Analysis Tool for the Intelligent Text Document Filtering. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2000. Lecture Notes in Computer Science(), vol 1902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45323-7_26
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DOI: https://doi.org/10.1007/3-540-45323-7_26
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