Reinvestigating the Classification Approach to the Article and Preposition Error Correction - Grundkiewicz, Roman; Junczys-Dowmunt, Marcin (LTC 2015: Human Language Technology. Challenges for Computer Science and Linguistics, 2017)
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.