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- W2789678961 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." @default.
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- W2789678961 date "2018-01-01" @default.
- W2789678961 modified "2023-09-26" @default.
- W2789678961 title "Document Ranking Applied to Second Language Learning" @default.
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- W2789678961 doi "https://doi.org/10.1007/978-3-319-76941-7_53" @default.
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