Exploring alignment-classification methods in the context of professional writing assistance

Abstract

Proofreading, the act of checking first-draft writings performed by native experts, is essential for professional writing by non-native speakers. Usually, proofreading experts return the corrected texts to the writer without reasons of correction, which makes it difficult for the writer to learn from their errors. The combination of word alignment and classification techniques can help us to analyze the original and corrected texts and use them for language learning. In this study, we explore different alignment-classification methods for this task. Our experimental results show that the best method achieved 71.8% in accuracy. We also propose a new error taxonomy for tagging learner corpora, and present our alignment-classification results on the corpus tagged with this new tagset.