Automatic text indexing using complex identifiers
DOCPROCS '88 Proceedings of the ACM conference on Document processing systems
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Mining topic-specific concepts and definitions on the web
WWW '03 Proceedings of the 12th international conference on World Wide Web
Personalizing Textbooks with Slicing Technologies - Concept, Tools, Architecture, Collaboration Use
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume
Adaptive sentence boundary disambiguation
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Reusing Learning Resources based on Semantic Web Technologies
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Learning with new technologies: Help seeking and information searching revisited
Computers & Education
Hi-index | 0.00 |
We propose, in this paper, a model that extracts automatically learning objects as response to a user request. To do this, we proceed by automatically annotating texts with semantic metadata. These metadata will allow us to index and extract learning objects from texts. Thus, our model is composed of two principal parts: the first part consists of a semantic annotation of learning objects according to their semantic categories definition, example, exercise, etc.. The second part uses automatic semantic annotation which is generated by the first part to create a semantic inverted index able to find relevant learning objects for queries associated with semantic categories. We add a secondary part to our model which sorts the results offered to the user according to their relevance. We have implemented a system called SRIDoP, on the basis of the proposed model and we have verified its effectiveness.