We investigate the use of web search queries for detecting errors in non-native writing. Distinguishing a correct sequence of words from a sequence with a learner error is a baseline task that any error detection and correction system needs to address. Using a large corpus of error-annotated learner data, we investigate whether web search result counts can be used to distinguish correct from incorrect usage. In this investigation, we compare a variety of query formulation strategies and a number of web resources, including two major search engine APIs and a large web-based n-gram corpus.

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In  Proceedings of NAACL 2010

Publisher  Association for Computational Linguistics
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