Abstract
Text classification is a supervised learning task for assigning text document to one or more predefined classes/topics. These topics are determined by a set of training documents. In order to construct a classification model, a machine learning algorithm was used. Training data is often a set of full-text documents. The training model is used to predict a class for new coming document. In this paper, we propose a text classification approach based on automatic text summarization. The proposed approach is tested with 2000 Vietnamese text documents downloaded from vnexpress.net and vietnamnet.vn. The experimental results confirm the feasibility of proposed model.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Truong, Q.D., Huynh, H.X., Nguyen, C.N. (2016). An Abstract-Based Approach for Text Classification. In: Vinh, P., Barolli, L. (eds) Nature of Computation and Communication. ICTCC 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 168. Springer, Cham. https://doi.org/10.1007/978-3-319-46909-6_22
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DOI: https://doi.org/10.1007/978-3-319-46909-6_22
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