Model Combination for Correcting Preposition Selection Errors
Nitin Madnani, Michael Heilman, Aoife Cahill · 2016
Many grammatical error correction approaches use classifiers with specially-engineered features to predict corrections.A simpler alternative is to use n-gram language model scores.Rozovskaya and Roth (2011) reported that classifiers outperformed a language modeling approach.Here, we report a more nuanced result: a classifier approach yielded results with higher precision while a language modeling approach provided better recall.Most importantly, we found that a combined approach using a logistic regression ensemble outperformed both a classifier and a language modeling approach.