Information Processing and Management: an International Journal
Integrating automatic genre analysis into digital libraries
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Toward an improved concept-based information retrieval system
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Web Document Classification Based on Fuzzy Association
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Developing Adaptive Internet Based Courses with the Authoring System NetCoach
Revised Papers from the nternational Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity
Document Comparison with a Weighted Topic Hierarchy
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Automatic detection of text genre
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automatic metadata generation based on neural network
InfoSecu '04 Proceedings of the 3rd international conference on Information security
Automatic summarisation of legal documents
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Automating metadata generation: the simple indexing interface
WWW '05 Proceedings of the 14th international conference on World Wide Web
Ontologies for Reusing Learning Object Content
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
DCMI '02 Proceedings of the 2002 international conference on Dublin core and metadata applications: Metadata for e-communities: supporting diversity and convergence
Making metadata go away: "hiding everything but the benefits"
DCMI '04 Proceedings of the 2004 international conference on Dublin Core and metadata applications: metadata across languages and cultures
Methods for domain-independent information extraction from the web: an experimental comparison
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
FIE'09 Proceedings of the 39th IEEE international conference on Frontiers in education conference
Automatic mining of cognitive metadata using fuzzy inference
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Automatic metadata mining from multilingual enterprise content
Web Semantics: Science, Services and Agents on the World Wide Web
iLOG: a framework for automatic annotation of learning objects with empirical usage metadata
International Journal of Artificial Intelligence in Education
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Annotating learning material with metadata allows easy reusability by different learning/tutoring systems. Several metadata standards have been developed to represent learning objects and courses. A learning system needs to use pedagogic attributes including document type, topic, coverage of concepts, and for each concept the significance and the role. Moreover, in order to have a flexible and reusable repository of e-learning materials, it is necessary that the annotation of the documents with such metadata be done in an automatic fashion as far as possible. This paper describes the attributes that represent some important pedagogic characteristics of learning materials. To reduce the overhead of manual annotation we have explored the feasibility of automatic annotation of learning materials with metadata. This facilitates the creation of an elearning open repository for storing these annotated learning materials, which can be used by learning systems. The automatic annotation is based on a domain knowledge base and a number of algorithms like standard classification algorithms, parsing and analysis of documents have been used for this purpose. The results show a fair degree of accuracy, which may be improved in future using more sophisticated algorithms.