TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
The best of two worlds: cooperation of statistical and rule-based taggers for Czech
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
Native judgments of non-native usage: experiments in preposition error detection
HumanJudge '08 Proceedings of the Workshop on Human Judgements in Computational Linguistics
Annotating ESL errors: challenges and rewards
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Error-tagged learner corpus of Czech
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Generating learner-like morphological errors in Russian
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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The paper describes a corpus of texts produced by non-native speakers of Czech. We discuss its annotation scheme, consisting of three interlinked tiers, designed to handle a wide range of error types present in the input. Each tier corrects different types of errors; links between the tiers allow capturing errors in word order and complex discontinuous expressions. Errors are not only corrected, but also classified. The annotation scheme is tested on a data set including approx. 175,000 words with fair inter-annotator agreement results. We also explore the possibility of applying automated linguistic annotation tools (taggers, spell checkers and grammar checkers) to the learner text to support or even substitute manual annotation.