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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Schmidt, S., Scholl, P., Rensing, C., Steinmetz, R. (2011). Cross-Lingual Recommendations in a Resource-Based Learning Scenario. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds) Towards Ubiquitous Learning. EC-TEL 2011. Lecture Notes in Computer Science, vol 6964. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23985-4_28
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DOI: https://doi.org/10.1007/978-3-642-23985-4_28
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