Abstract
In this research, we attempt to apply the NTC (Neural Text Categorizer) to the text categorization without decomposing it into binary classifications. Because a single classifier has its very weak robustness to the entire text categorization, it is usually decomposed into binary classifications as many as categories. However, it requires to rearrange and relabel the given training examples with positive or negative labels for decomposing the text categorization. The task of this research is to apply the NTC to the text categorization without the decomposition and validate its feasibility. Therefore, we will compare the NTC with other approaches in the text categorization in the environment where the text categorization is not decomposed and validate that the NTC is practical tool for implement a light version of text categorization system.
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Jo, T. (2009). Categorizing News Articles Using NTC without Decomposition. In: Ślęzak, D., Kim, Th., Zhang, Y., Ma, J., Chung, Ki. (eds) Database Theory and Application. DTA 2009. Communications in Computer and Information Science, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10583-8_5
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DOI: https://doi.org/10.1007/978-3-642-10583-8_5
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