Abstract
In this paper, we focus on text categorization model by unsupervised learning techniques that do not require labeled data. We propose a feature learning bootstrapping algorithm (FLB) using a small number of seed words, in that features for each of categories could be automatically learned from a large amount of unlabeled documents. Using these learned features we develop a new Naïve Bayes classifier named NB_FLB. Experimental results show that the NB_FLB classifier performs better than other Naïve Bayes classifiers by supervised learning in small number of features cases.
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Jingbo, Z., Wenliang, C., Tianshun, Y. (2004). Using Seed Words to Learn to Categorize Chinese Text. In: Vicedo, J.L., Martínez-Barco, P., Muńoz, R., Saiz Noeda, M. (eds) Advances in Natural Language Processing. EsTAL 2004. Lecture Notes in Computer Science(), vol 3230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30228-5_41
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DOI: https://doi.org/10.1007/978-3-540-30228-5_41
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