Abstract
Many stemming techniques are used in the context of Arabic Text Classification. In this paper, we show the effect of stemming on classification systems. We introduce a new stemming technique -approximate stemming- based on the use of Arabic patterns. These patterns are modeled using transducers and stemming is done without depending on any dictionary. Using transducers for stemming words, documents are transformed into finite state transducers. This allow us to use rational kernels as a framework for Arabic Text Classification. Experiments show that, when compared with other approaches, our approach is more effective specially in term of Accuracy, Recall and F1.
This work is supported by the MESRS - Algeria under Project 8/U03/7015.
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Nehar, A., Ziadi, D., Cherroun, H. (2013). Rational Kernels for Arabic Text Classification. In: Dediu, AH., Martín-Vide, C., Mitkov, R., Truthe, B. (eds) Statistical Language and Speech Processing. SLSP 2013. Lecture Notes in Computer Science(), vol 7978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39593-2_16
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DOI: https://doi.org/10.1007/978-3-642-39593-2_16
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