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- W4385570175 abstract "We introduce the Korean-Learner-Morpheme (KLM) corpus, a manually annotated dataset consisting of 129,784 morphemes from second language (L2) learners of Korean, featuring morpheme tokenization and part-of-speech (POS) tagging. We evaluate the performance of four Korean morphological analyzers in tokenization and POS tagging on the L2- Korean corpus. Results highlight the analyzers’ reduced performance on L2 data, indicating the limitation of advanced deep-learning models when dealing with L2-Korean corpora. We further show that fine-tuning one of the models with the KLM corpus improves its accuracy of tokenization and POS tagging on L2-Korean dataset." @default.
- W4385570175 created "2023-08-05" @default.
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- W4385570175 date "2023-01-01" @default.
- W4385570175 modified "2023-09-24" @default.
- W4385570175 title "Towards L2-friendly pipelines for learner corpora: A case of written production by L2-Korean learners" @default.
- W4385570175 doi "https://doi.org/10.18653/v1/2023.bea-1.6" @default.
- W4385570175 hasPublicationYear "2023" @default.
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