Abstract
When analyzing language acquisition of inflective languages like Czech, it is necessary to distinguish between errors in word stems and errors in inflection. We use the data of the learner corpus CzeSL, but we propose a simpler error classification based on levels of language description (orthography, morphonology, morphology, syntax, lexicon), which takes into account the uncertainty about the causes of the error. We present a rule-based automatic annotation tool, which can assist both the task of manual error classification and stochastic automatic error annotation with preliminary results of types of errors related to the language proficiency of the text authors.
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Acknowledgments
This research was supported by the Grant Agency of the Czech Republic through the grant 16-10185S (Non-native Czech from the Theoretical and Computational Perspective).
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Jelínek, T. (2017). Errors in Inflection in Czech as a Second Language and Their Automatic Classification. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_30
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DOI: https://doi.org/10.1007/978-3-319-64206-2_30
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