Abstract
In this paper we present a simple approach for Vietnamese text classification without word segmentation, based on frequent subgraph mining techniques. A graph-based instead of traditional vector-based model is used for document representation. The classification model employs structural patterns (subgraphs) and Dice measure of similarity to identify a class of documents. This method is evaluated on Vietnamese data set for measuring classification accuracy. Results show that it can outperform k-NN algorithm (based on vector, hybrid document representation) in terms of accuracy and classification time.
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Nguyen, T.A.H., Hoang, K. (2009). Frequent Subgraph-Based Approach for Classifying Vietnamese Text Documents. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2009. Lecture Notes in Business Information Processing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01347-8_25
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DOI: https://doi.org/10.1007/978-3-642-01347-8_25
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