Adriane Boyd and Detmar Meurers
Proceedings of the 13th European Workshop on Natural Language Generation (ENLG) -- Helping Our Own (HOO) Challenge. Nancy, France. 267--269.
We extend the n-gram-based data-driven prediction approach (Elghafari, Meurers and Wunsch, 2010) to identify function word errors in non-native academic texts as part of the Helping Our Own (HOO) Shared Task. We focus on substitution errors for four categories: prepositions, determiners, conjunctions, and quantifiers. These error types make up 12% of the errors annotated in the HOO training data.
In our best submission in terms of the error detection score, we detected 67% of preposition and determiner substitution errors, 40% of conjunction substitution errors, and 33% of quantifier substitution errors. For approximately half of the errors detected, we were also able to provide an appropriate correction.
Electronically available:
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Bibtex entry:
@InProceedings{Boyd.Meurers-11,
author = {Boyd, Adriane and Meurers, Detmar},
title = {Data-Driven Correction of Function Words in Non-Native English},
booktitle = {Proceedings of the 13th European Workshop
on Natural Language Generation -- Helping Our Own (HOO)
Challenge},
year = {2011},
pages = {267--269}
address = {Nancy, France},
publisher = {Association for Computational Linguistics},
url = {http://purl.org/dm/papers/boyd-meurers-11.html}
}