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- W2252000648 abstract "This paper explores the generation of artificial errors for correcting grammatical mistakes made by learners of English as a second language. Artificial errors are injected into a set of error-free sentences in a probabilistic manner using statistics from a corpus. Unlike previous approaches, we use linguistic information to derive error generation probabilities and build corpora to correct several error types, including open-class errors. In addition, we also analyse the variables involved in the selection of candidate sentences. Experiments using the NUCLE corpus from the CoNLL 2013 shared task reveal that: 1) training on artificially created errors improves precision at the expense of recall and 2) different types of linguistic information are better suited for correcting different error types." @default.
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- W2252000648 date "2014-01-01" @default.
- W2252000648 modified "2023-09-26" @default.
- W2252000648 title "Generating artificial errors for grammatical error correction" @default.
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- W2252000648 doi "https://doi.org/10.3115/v1/e14-3013" @default.
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