A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction

Michael Flor, Michael Fried, Alla Rozovskaya


Abstract
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.
Anthology ID:
W19-4407
Volume:
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
76–86
Language:
URL:
https://aclanthology.org/W19-4407
DOI:
10.18653/v1/W19-4407
Bibkey:
Cite (ACL):
Michael Flor, Michael Fried, and Alla Rozovskaya. 2019. A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 76–86, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction (Flor et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-4407.pdf
Code
 EducationalTestingService/toefl-spell
Data
MIMIC-III