The nature of statistical learning theory
The nature of statistical learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Reusable learning objects: a survey of LOM-based repositories
Proceedings of the tenth ACM international conference on Multimedia
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Strategies for automatic LOM metadata generating in a web-based CSCL tool
WebMedia '05 Proceedings of the 11th Brazilian Symposium on Multimedia and the web
Predicting reading difficulty with statistical language models
Journal of the American Society for Information Science and Technology
Syntactic simplification for improving content selection in multi-document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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In this article, we explore the task of automatically identifying educational materials by classifying documents with respect to their educational value. Through experiments carried out on a dataset of manually annotated documents, we show that the generally accepted notion of a learning object's “educational value” is indeed a property that can be reliably assigned through automatic classification. Moreover, an analysis of cross-topic and cross-domain portability shows that the automatic classifier can be ported to other topics and domains, with minimal performance loss.