The mathematics of statistical machine translation: parameter estimation
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An unsupervised method for detecting grammatical errors
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Countability and number in Japanese to English machine translation
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PENS: a machine-aided english writing system for Chinese users
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Phrasal cohesion and statistical machine translation
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Automated rating of ESL essays
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Dependency treelet translation: syntactically informed phrasal SMT
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Language Resources and Evaluation
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This paper presents a pilot study of the use of phrasal Statistical Machine Translation (SMT) techniques to identify and correct writing errors made by learners of English as a Second Language (ESL). Using examples of mass noun errors found in the Chinese Learner Error Corpus (CLEC) to guide creation of an engineered training set, we show that application of the SMT paradigm can capture errors not well addressed by widely-used proofing tools designed for native speakers. Our system was able to correct 61.81% of mistakes in a set of naturally-occurring examples of mass noun errors found on the World Wide Web, suggesting that efforts to collect alignable corpora of pre- and post-editing ESL writing samples offer can enable the development of SMT-based writing assistance tools capable of repairing many of the complex syntactic and lexical problems found in the writing of ESL learners.