Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
HLT '01 Proceedings of the first international conference on Human language technology research
A finite-state morphological grammar of hebrew
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
Paraphrase alignment for synonym evidence discovery
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A new dataset and method for automatically grading ESOL texts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Algorithm selection and model adaptation for ESL correction tasks
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Joint Hebrew segmentation and parsing using a PCFG-LA lattice parser
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
On Morphological Analysis for Learner Language, Focusing on Russian
Research on Language and Computation
Developing methodology for Korean particle error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Helping our own: the HOO 2011 pilot shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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We develop a system for predicting the level of language learners, using only a small amount of targeted language data. In particular, we focus on learners of Hebrew and predict level based on restricted placement exam exercises. As with many language teaching situations, a major problem is data sparsity, which we account for in our feature selection, learning algorithm, and in the setup. Specifically, we define a two-phase classification process, isolating individual errors and linguistic constructions which are then aggregated into a second phase; such a two-step process allows for easy integration of other exercises and features in the future. The aggregation of information also allows us to smooth over sparse features.