Automatic syllabus classification

ABSTRACT

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.

REFERENCES

• 1: Thorsten Joachims, Text Categorization with Suport Vector Machines: Learning with Many Relevant Features, Proceedings of the 10th European Conference on Machine Learning, p.137-142, April 21-23, 1998
• 2: Alistair Kennedy , Michael Shepherd, Automatic Identification of Home Pages on the Web, Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4, p.99.3, January 03-06, 2005 [doi>10.1109/HICSS.2005.114]
• 3: Sang-Bum Kim , Kyoung-Soo Han , Hae-Chang Rim , Sung Hyon Myaeng, Some Effective Techniques for Naive Bayes Text Classification, IEEE Transactions on Knowledge and Data Engineering, v.18 n.11, p.1457-1466, November 2006 [doi>10.1109/TKDE.2006.180]
• 4: Manas Tungare , Xiaoyan Yu , William Cameron , GuoFang Teng , Manuel A. Pérez-Quiñones , Lillian Cassel , Weiguo Fan , Edward A. Fox, Towards a syllabus repository for computer science courses, Proceedings of the 38th SIGCSE technical symposium on Computer science education, March 07-11, 2007, Covington, Kentucky, USA

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries

General Terms:
Design, Experimentation, Performance

Keywords:
SVM, genre, syllabus, text classification