In this work, we reinvestigate the classifier-based approach to article and preposition error correction going beyond linguistically mo-tivated 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 are the first to propose and 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this work, we reinvestigate the classifier-based approach to article and preposition error correction going beyond linguistically mo-tivated 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 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