Abstract
Usually, in traditional text categorization systems based on Vector Space Model, there is no context information in a feature vector, which limited the performance of the system. To make use of more information, it is natural to select bi-gram feature in addition to unigram feature. However, the longer the feature is, the more important the feature selection algorithm is to get good balance in feature space This paper proposed two feature extraction methods which can get better feature balance for document categorization. Experiments show that our extended bi-gram feature improved system performance greatly.
This paper is supported by Natural Science Foundation No.60272019 and No. 60321002
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Zhang, X., Zhu, X. (2005). Extended Bi-gram Features in Text Categorization. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_47
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DOI: https://doi.org/10.1007/11492542_47
Publisher Name: Springer, Berlin, Heidelberg
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