Matthieu Hermet


Using First and Second Language Models to Correct Preposition Errors in Second Language Authoring
Matthieu Hermet | Alain Désilets
Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications

pdf bib
Using Automatic Roundtrip Translation to Repair General Errors in Second Language Writing
Alain Désilets | Matthieu Hermet
Proceedings of Machine Translation Summit XII: Posters


Using the Web as a Linguistic Resource to Automatically Correct Lexico-Syntactic Errors
Matthieu Hermet | Alain Désilets | Stan Szpakowicz
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper presents an algorithm for correcting language errors typical of second-language learners. We focus on preposition errors, which are very common among second-language learners but are not addressed well by current commercial grammar correctors and editing aids. The algorithm takes as input a sentence containing a preposition error (and possibly other errors as well), and outputs the correct preposition for that particular sentence context. We use a two-phase hybrid rule-based and statistical approach. In the first phase, rule-based processing is used to generate a short expression that captures the context of use of the preposition in the input sentence. In the second phase, Web searches are used to evaluate the frequency of this expression, when alternative prepositions are used instead of the original one. We tested this algorithm on a corpus of 133 French sentences written by intermediate second-language learners, and found that it could address 69.9% of those cases. In contrast, we found that the best French grammar and spell checker currently on the market, Antidote, addressed only 3% of those cases. We also showed that performance degrades gracefully when using a corpus of frequent n-grams to evaluate frequencies.