Abstract
In this paper, we explore the artificial generation of typographical errors based on real-world statistics. We first draw on a small set of annotated data to compute spelling error statistics. These are then invoked to introduce errors into substantially larger corpora. The generation methodology allows us to generate particularly challenging errors that require context-aware error detection. We use it to create a set of English language error detection and correction datasets. Finally, we examine the effectiveness of machine learning models for detecting and correcting errors based on this data.- Anthology ID:
- 2020.lrec-1.856
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6930–6936
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.856
- DOI:
- Cite (ACL):
- Kshitij Shah and Gerard de Melo. 2020. Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6930–6936, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation (Shah & de Melo, LREC 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.856.pdf
- Data
- IMDb Movie Reviews