eLearn
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
How do you feel about "dancing queen"?: deriving mood & theme annotations from user tags
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Automated Educational Course Metadata Generation Based on Semantics Discovery
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Little search game: term network acquisition via a human computation game
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
Personalized Text Summarization Based on Important Terms Identification
DEXA '12 Proceedings of the 2012 23rd International Workshop on Database and Expert Systems Applications
Managing content, metadata and user-created annotations in web-based applications
Proceedings of the 2013 ACM symposium on Document engineering
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Automated acquisition of relevant domain terms from educational documents available in social educational systems can benefit from processing a growing number of user-created annotations assigned to the content. Annotations provide us potentially useful information about documents and can improve the results of base Automatic Term Recognition (ATR) algorithms. We propose a method for relevant domain terms extraction based on user-created annotations processing. We consider three basic annotation types: tags, comments and highlights. The final term weight is computed by combining relevant domain terms weights obtained from the individual annotation types and those obtained from the text. The method was evaluated using data from Principles of Software Engineering course in adaptive educational system ALEF and showed that enhancements based on annotation processing yield significant improvement of results.