Abstract
In the probabilistic model of text categorization, we assume that terms are characterized by their statistical distribution of tf-idf metrics. However, we feel that classical tf-idf metrics may not be the best method solution for information retrieval and text categorization. We explored a language modeling approach with term-specific weighting method to improve the performance of text categorization system. To make our method comparable to the previous approaches, we performed an experiment and compared it to basic models. Term-specific language modeling approach to text categorization problem significantly outperformed the baseline model on each point of the evaluation.
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Advanced Information Technology Research Center(AITrc).
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Kang, SS. (2004). Term-Specific Language Modeling Approach to Text Categorization. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_79
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DOI: https://doi.org/10.1007/978-3-540-24768-5_79
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