%0 Conference Proceedings %T A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction %A Flor, Michael %A Fried, Michael %A Rozovskaya, Alla %Y Yannakoudakis, Helen %Y Kochmar, Ekaterina %Y Leacock, Claudia %Y Madnani, Nitin %Y Pilán, Ildikó %Y Zesch, Torsten %S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications %D 2019 %8 August %I Association for Computational Linguistics %C Florence, Italy %F flor-etal-2019-benchmark %X Spelling correction has attracted a lot of attention in the NLP community. However, models have been usually evaluated on artificiallycreated or proprietary corpora. A publiclyavailable corpus of authentic misspellings, annotated in context, is still lacking. To address this, we present and release an annotated data set of 6,121 spelling errors in context, based on a corpus of essays written by English language learners. We also develop a minimallysupervised context-aware approach to spelling correction. It achieves strong results on our data: 88.12% accuracy. This approach can also train with a minimal amount of annotated data (performance reduced by less than 1%). Furthermore, this approach allows easy portability to new domains. We evaluate our model on data from a medical domain and demonstrate that it rivals the performance of a model trained and tuned on in-domain data. %R 10.18653/v1/W19-4407 %U https://aclanthology.org/W19-4407 %U https://doi.org/10.18653/v1/W19-4407 %P 76-86