Research Article
An Abstract-Based Approach for Text Classification
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@INPROCEEDINGS{10.1007/978-3-319-46909-6_22, author={Quoc Truong and Hiep Huynh and Cuong Nguyen}, title={An Abstract-Based Approach for Text Classification}, proceedings={Nature of Computation and Communication. Second International Conference, ICTCC 2016, Rach Gia, Vietnam, March 17-18, 2016, Revised Selected Papers}, proceedings_a={ICTCC}, year={2017}, month={1}, keywords={Text classification Automatic text summarization Machine learning}, doi={10.1007/978-3-319-46909-6_22} }- Quoc Truong
Hiep Huynh
Cuong Nguyen
Year: 2017
An Abstract-Based Approach for Text Classification
ICTCC
Springer
DOI: 10.1007/978-3-319-46909-6_22
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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