GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
This provides a new, rich resource for NLP researchers and developers working on spelling correction and grammatical error correction tasks, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of large-scale datasets for spelling and grammatical error correction by creating the GitHub Typo Corpus, a multilingual dataset with over 350k edits and 65M characters in more than 15 languages, which they made publicly available.
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). 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. 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. 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 detailed analyses of the dataset show that existing spelling correctors merely achieve an F-measure of approx. 0.5, suggesting that the dataset serves as a new, rich source of spelling errors that complement existing datasets.