Model Combination for Correcting Preposition Selection Errors

نویسندگان

  • Nitin Madnani
  • Michael Heilman
  • Aoife Cahill
چکیده

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.

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تاریخ انتشار 2016