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Cross-Lingual Recommendations in a Resource-Based Learning Scenario

Author:Sebastian Schmidt, Philipp Scholl, Christoph Rensing, Ralf Steinmetz
Date:September 2011
Kind:In proceedings - use for conference & workshop papers
Book title:Towards Ubiquitious Learning, Proceedings of the 6th European Conference on Technology Enhanced Learning, EC-TEL 2011
Editor:Carlos Delgado Kloos, Denis Gillet, Raquel M. Crespo Garcia, Fridolin Wild, Martin Wolpers
Number:LNCS 6964
Keywords:Explicit Semantic Analysis, Cross-Language Semantic Relatedness, Wikipedia, Reference Corpus, Recommendation
Number of characters:32768
Research Area(s):Knowledge Media
Abstract:CROKODIL is a platform supporting resource--based learning scenarios for self--directed, on--task learning with web resources. As CROKODIL enables the forming of possibly large learning communities, the stored data is growing in a large scale. Thus, an appropriate recommendation of tags and learning resources becomes increasingly important for supporting learners. We propose semantic relatedness between tags and resources as a basis of recommendation and identify Explicit Semantic Analysis (ESA) using Wikipedia as reference corpus as a viable option. However, data from CROKODIL shows that tags and resources are often composed in different languages. Thus, a monolingual approach to provide recommendations is not applicable in CROKODIL. Thus, we examine strategies for providing mappings between different languages, extending ESA to provide cross-lingual capabilities. Specifically, we present mapping strategies that utilize additional semantic information contained in Wikipedia. Based on CROKODIL's application scenario, we present an evaluation design and show results of cross-lingual ESA.
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