Conference proceeding
Using automatic metadata extraction to build a structured syllabus repository
ASIAN DIGITAL LIBRARIES: LOOKING BACK 10 YEARS AND FORGING NEW FRONTIERS, PROCEEDINGS, Vol.4822, pp.337-346
Lecture Notes in Computer Science
01/01/2007
DOI: 10.1007/978-3-540-77094-7_43
Abstract
Syllabi are important documents created by instructors for students. Gathering syllabi that are freely available, and creating useful services on top of the collection, will yield a digital library of value for the educational community. However, gathering and building a repository of syllabi is complicated by the unstructured nature of syllabus representation and the lack of a unified vocabulary for syllabus construction. In this paper, we propose an intelligent approach to automatically annotate freely-available syllabi from the Web to benefit the educational community through supporting services such as semantic search. We discuss our detailed process for converting unstructured syllabi to structured representations through entity recognition, segmentation, and association. Our evaluation results demonstrate the effectiveness of our extractor and also suggest improvements. We hope our work will benefit not only users of our services but also people who are interested in building other genre-specific repositories.
Details
- Title: Subtitle
- Using automatic metadata extraction to build a structured syllabus repository
- Creators
- Xiaoyan Yu - Virginia TechManas Tungare - Virginia TechWeiguo Fan - Virginia TechManuel Perez-Quinones - Virginia TechEdward A. Fox - Virginia TechWilliam Cameron - Villanova UniversityLillian Cassel - Villanova University
- Contributors
- DHL Goh (Editor)T H Cao (Editor)I T Solvberg (Editor)E Rasmussen (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- ASIAN DIGITAL LIBRARIES: LOOKING BACK 10 YEARS AND FORGING NEW FRONTIERS, PROCEEDINGS, Vol.4822, pp.337-346
- Publisher
- Springer Nature
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-540-77094-7_43
- ISSN
- 0302-9743
- eISSN
- 1611-3349
- Number of pages
- 3
- Grant note
- 0532825 / National Science Foundation under DUE
- Language
- English
- Date published
- 01/01/2007
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380540202771
Metrics
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