Publication at Faculty of Arts |
2017
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.