Adriane Boyd, Marion Zepf and Detmar Meurers
Proceedings of the 7th Workshop on Innovative Use of NLP for Building Educational Applications (BEA7). 2012..
We extend our n-gram-based data-driven prediction approach from the Helping Our Own (HOO) 2011 Shared Task (Boyd and Meurers, 2011) to identify determiner and preposition errors in non-native English essays from the Cambridge Learner Corpus FCE Dataset (Yannakoudakis et al., 2011) as part of the HOO 2012 Shared Task. Our system focuses on three error categories: missing determiner, incorrect determiner, and incorrect preposition. Approximately two-thirds of the errors annotated in HOO 2012 training and test data fall into these three categories. To improve our approach, we developed a missing determiner detector and incorporated word clustering (Brown et al., 1992) into the n-gram prediction approach.
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Bibtex entry:
@InProceedings{Boyd.Zepf.Meurers-12,
author = {Adriane Boyd and Marion Zepf and Detmar Meurers},
title = {Informing Determiner and Preposition Error Correction
with Hierarchical Word Clustering},
booktitle = {Proceedings of the 7th Workshop on
Innovative Use of NLP for Building Educational Applications
(BEA7)},
year = {2012},
address = {Montreal, Canada},
publisher = {Association for Computational Linguistics},
pages = {208--215},
url = {http://purl.org/dm/papers/boyd-zepf-meurers-12.html}
}