Detecting errors in English article usage by non-native speakers
Natural Language Engineering
The ups and downs of preposition error detection in ESL writing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Automated Grammatical Error Detection for Language Learners
Automated Grammatical Error Detection for Language Learners
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
Building a Korean web corpus for analyzing learner language
WAC-6 '10 Proceedings of the NAACL HLT 2010 Sixth Web as Corpus Workshop
Factors affecting the accuracy of Korean parsing
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Correcting comma errors in learner essays, and restoring commas in newswire text
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Predicting learner levels for online exercises of Hebrew
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Developing learner corpus annotation for Korean particle errors
LAW VI '12 Proceedings of the Sixth Linguistic Annotation Workshop
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We further work on detecting errors in post-positional particle usage by learners of Korean by improving the training data and developing a complete pipeline of particle selection. We improve the data by filtering non-Korean data and sampling instances to better match the particle distribution. Our evaluation shows that, while the data selection is effective, there is much work to be done with preprocessing and system optimization.