In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6761–6768, Marseille, France. GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors. Anthology ID: 2020.lrec-1.835 Volume: Proceedings of the Twelfth Language Resources and Evaluation Conference Month: May Year: 2020 Address: Marseille, France Venue: LREC SIG: Publisher: European Language Resources Association Note: Pages: 6761–6768 Language: English URL: DOI: Bibkey: hagiwara-mita-2020-github Cite (ACL): Masato Hagiwara and Masato Mita. 0.5, suggesting that the dataset serves as a new, rich source of spelling errors that complement existing datasets. The detailed analyses of the dataset show that existing spelling correctors merely achieve an F-measure of approx. We also describe our process for filtering true typo edits based on learned classifiers on a small annotated subset, and demonstrate that typo edits can be identified with F1 0.9 using a very simple classifier with only three features.

The dataset, which we have made publicly available, contains more than 350k edits and 65M characters in more than 15 languages, making it the largest dataset of misspellings to date.

As a complementary new resource for these tasks, we present the GitHub Typo Corpus, a large-scale, multilingual dataset of misspellings and grammatical errors along with their corrections harvested from GitHub, a large and popular platform for hosting and sharing git repositories. Abstract The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC).

Typo mistakes