Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Automatic Identification of Home Pages on the Web
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
Some Effective Techniques for Naive Bayes Text Classification
IEEE Transactions on Knowledge and Data Engineering
Towards a syllabus repository for computer science courses
Proceedings of the 38th SIGCSE technical symposium on Computer science education
Development of a National Syllabus Repository for Higher Education in Ireland
ECDL '08 Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries
Using automatic metadata extraction to build a structured syllabus repository
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
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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.