Abstract
This paper addresses the needs of language learners and teachers by combining keyword-based search and language level information on an algorithm that can rank documents by pertinence to the required topic (keywords) and adequacy to the user’s language level. We conducted several experiments using the EF-CAMDAT corpus (annotated for topic and level) and we observed that the best ranking results were an average of BM25 and linguistic information. We also saw that the grammar of level C1 is the best indicator for level. Finally, we proposed a customization for prioritizing beginner or intermediate levels at the top of the rank.
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Notes
- 1.
For the grammar and vocabulary of level C1, we used the inversed score, because we expect that an A-level document would present them in a lesser degree.
- 2.
In this work we did not address advanced-level learners, because they are supposed to use traditional search engine systems.
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Acknowledgements
We would like to thank the Walloon Region (Projects BEWARE n. 1510637 and 1610378) for support, and Altissia International for research collaboration.
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Wilkens, R., Zilio, L., Fairon, C. (2018). Document Ranking Applied to Second Language Learning. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_53
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