Robust Systems for Preposition Error Correction Using Wikipedia Revisions

Aoife Cahill, Nitin Madnani, Joel R. Tetreault, Diane Napolitano

Abstract: We show that existing methods for training preposition error correction systems, whether using well-edited text or error-annotated corpora, do not generalize across very different test sets. We present a new, large errorannotated corpus and use it to train systems that generalize across three different test sets, each from a different domain and with different error characteristics. This new corpus is automatically extracted from Wikipedia revisions and contains over one million instances of preposition corrections.