Examining the Use of Region Web Counts for ESL Error Detection

Significant work is being done to develop NLP systems that can detect writing errors produced by non-native English speakers. A major issue, however, is the lack of available error-annotated training data needed to build statistical models that drive these major systems. As a result, many systems are trained on well-formed text with no modeling of typical errors that non-native speakers produce. To address this issue, we propose a novel method of using geographic region-specific web counts to detect typical errors in the writing of non-native speakers. In this paper we describe the approach, and present an analysis of the issues involved when using web counts.