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
The role of PP attachment in preposition generation
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Why is zero marking important in korean?
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Developing methodology for Korean particle error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
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Post-positional particles are a significant source of errors for learners of Korean. Following methodology that has proven effective in handling English preposition errors, we are beginning the process of building a machine learner for particle error detection in L2 Korean writing. As a first step, however, we must acquire data, and thus we present a methodology for constructing large-scale corpora of Korean from the Web, exploring the feasibility of building corpora appropriate for a given topic and grammatical construction.