@inproceedings{grundkiewicz2017reinvestigating,
author = {Grundkiewicz, Roman and Junczys-Dowmunt, Marcin},
title = {Reinvestigating the Classification Approach to the Article and Preposition Error Correction},
booktitle = {LTC 2015: Human Language Technology. Challenges for Computer Science and Linguistics},
year = {2017},
month = {February},
abstract = {In this work, we reinvestigate the classifier-based approach to article and preposition error correction going beyond linguistically motivated factors. We show that state-of-the-art results can be achieved without relying on a plethora of heuristic rules, complex feature engineering and advanced NLP tools. A proposed method for detecting spaces for article insertion is even more efficient than methods that use a parser. We examine automatically trained word classes acquired by unsupervised learning as a substitution for commonly used part-of-speech tags. Our best models significantly outperform the top systems from CoNLL-2014 Shared Task in terms of article and preposition error correction.},
url = {http://approjects.co.za/?big=en-us/research/publication/reinvestigating-classification-approach-article-preposition-error-correction/},
pages = {112-122},
}