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Automatic Syllabus Classification Using Support Vector Machines
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Author(s): Xiaoyan Yu (Virginia Tech, USA), Manas Tungare (Virginia Tech, USA), Weigo Yuan (Virginia Tech, USA), Yubo Yuan (Virginia Tech, USA), Manuel Pérez-Quiñones (Virginia Tech, USA)and Edward A. Fox (Virginia Tech, USA)
Copyright: 2009
Pages: 14
Source title:
Handbook of Research on Text and Web Mining Technologies
Source Author(s)/Editor(s): Min Song (New Jersey Institute of Technology, USA)and Yi-Fang Brook Wu (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59904-990-8.ch004
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Abstract
Syllabi are important educational resources. Gathering syllabi that are freely available and creating useful services on top of the collection presents great value for the educational community. However, searching for a syllabus on the Web using a generic search engine is an error-prone process and often yields too many irrelevant links. In this chapter, we describe our empirical study on automatic syllabus classification using support vector machines (SVM) to filter noise out from search results. We describe various steps in the classification process from training data preparation, feature selection, and classifier building using SVMs. Empirical results are provided and discussed. We hope our reported work will also benefit people who are interested in building other genre-specific repositories.
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