{"id":469020,"date":"2018-02-23T09:56:45","date_gmt":"2018-02-23T17:56:45","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=469020"},"modified":"2018-10-16T22:29:25","modified_gmt":"2018-10-17T05:29:25","slug":"reinvestigating-classification-approach-article-preposition-error-correction","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/reinvestigating-classification-approach-article-preposition-error-correction\/","title":{"rendered":"Reinvestigating the Classification Approach to the Article and Preposition Error Correction"},"content":{"rendered":"<div>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.<\/div>\n","protected":false},"excerpt":{"rendered":"<p>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 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