Conference proceeding
Automatic Syllabus Classification: BUILDING & SUSTAINING THE DIGITAL ENVIRONMENT
PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, pp.440-441
ACM-IEEE Joint Conference on Digital Libraries JCDL
01/01/2007
DOI: 10.1145/1255175.1255265
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 Naive Bayes. Empirical results indicate that the best version of our method achieves a high classification accuracy, i.e., an F-1 value of 83% on average.
Details
- Title: Subtitle
- Automatic Syllabus Classification: BUILDING & SUSTAINING THE DIGITAL ENVIRONMENT
- Creators
- Xiaoyan Yu - Virginia TechManas Tungare - Virginia TechWeiguo Fan - Virginia TechManuel Perez-Quinones - Virginia TechEdward A. Fox - Virginia TechWilliam Cameron - Villanova UniversityGuoFang Teng - Villanova UniversityLillian Cassel - Villanova University
- Contributors
- R Larson (Editor)E Rasmussen (Editor)S Sugimoto (Editor)E Toms (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, pp.440-441
- Publisher
- Assoc Computing Machinery
- Series
- ACM-IEEE Joint Conference on Digital Libraries JCDL
- DOI
- 10.1145/1255175.1255265
- ISSN
- 2575-7865
- eISSN
- 2575-8152
- Number of pages
- 2
- Language
- English
- Date published
- 01/01/2007
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380513602771
Metrics
2 Record Views