Automatic syllabus classification
Xiaoyan Yu, Manas Tungare, Weiguo Fan, Manuel A. Pérez-Quiñones, Edward A. Fox, William Cameron, GuoFang Teng, Lillian N Cassel · 2007
Syllabi are important educational resources. However, searching for a syllabus on the Web using a generic search engine is an error-prone process and often yields too many non-relevant links. In this paper, we present a syllabus classifier to filter noise out from search results. We discuss various steps in the classification process, including class definition, training data preparation, feature selection, and classifier building using SVM and Naïve Bayes. Empirical results indicate that the best version of our method achieves a high classification accuracy, i.e., an F1 value of 83% on average.