Detecting errors in English article usage by non-native speakers
Natural Language Engineering
A classifier-based approach to preposition and determiner error correction in L2 English
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
The ups and downs of preposition error detection in ESL writing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Using mostly native data to correct errors in learners' writing: a meta-classifier approach
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for 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
Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk
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
Developing methodology for Korean particle error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Annotating particle realization and ellipsis in Korean
LAW VI '12 Proceedings of the Sixth Linguistic Annotation Workshop
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We aim to sufficiently define annotation for post-positional particle errors in L2 Korean writing, so that future work on automatic particle error detection can make progress. To achieve this goal, we outline the linguistic properties of Korean particles in learner data. Given the agglutinative nature of Korean and the range of functions of particles, this annotation effort involves issues such as defining the tokens and target forms.