Abstract
This paper presents a Bayes document classifier using phrases as features. The phrases are extracted using a grammar that iteratively applies the rules to the sequence of words in the document. This grammar is generated from a training set using statistical word association. We report an improvement in the classification over the “bag of words” representation.
This work was partially supported by NSERC strategic grant and TL-NCE.
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© 2002 Springer-Verlag Berlin Heidelberg
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Bakus, J., Kamel, M. (2002). Document Classification Using Phrases. In: Caelli, T., Amin, A., Duin, R.P.W., de Ridder, D., Kamel, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2002. Lecture Notes in Computer Science, vol 2396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-70659-3_58
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DOI: https://doi.org/10.1007/3-540-70659-3_58
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