Automatic rule induction for unknown-word guessing
Computational Linguistics
Recognizing syntactic errors in the writing of second language learners
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Two-level morphology with composition
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Parsing Ill-Formed Text Using an Error Grammar
Artificial Intelligence Review
Conceptualizing Student Models for ICALL
UM '07 Proceedings of the 11th international conference on User Modeling
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Native judgments of non-native usage: experiments in preposition error detection
HumanJudge '08 Proceedings of the Workshop on Human Judgements in Computational Linguistics
A large-scale inheritance-based morphological lexicon for Russian
MorphSlav '03 Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages
Developing online ICALL exercises for Russian
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
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
Generating learner-like morphological errors in Russian
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Predicting learner levels for online exercises of Hebrew
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Hi-index | 0.00 |
We describe a framework for performing morphological analysis to account for learner language, focusing on Russian as an example of an inflecting language. Because a set of linguistic analyses is needed to provide feedback on potentially noisy data, there is a large amount of ambiguity for even well-formed words. Using a segmented POS lexicon as a test case, we show how to analyze subparts of words, in order to analyze variations. After describing and implementing this framework for Russian, we focus on removing undesirable analyses to keep the task feasible. This is essentially an investigation of how much overgeneration of analyses is a problem and under what assumptions it can be reduced.