Abstract
Text Classification is an important field of research. There are a number of approaches to classify text documents. However, there is an important challenge to improve the computational efficiency and recall. In this paper, we propose a novel framework to segment Chinese words, generate word vectors, train the corpus and make prediction. Based on the text classification technology, we successfully help the Chinese disabled persons to acquire job opportunities efficiently in real word. The results show that using this method to build the classifier yields better results than traditional methods. We also experimentally show that careful selection of a subset of features to represent the documents can improve the performance of the classifiers.
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Zhang, S., Gu, M. (2010). Improved Text Classification Technique to Acquire Job Opportunities for Disabled Persons. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2010. Communications in Computer and Information Science, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16388-3_31
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DOI: https://doi.org/10.1007/978-3-642-16388-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16387-6
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