Correcting the autocorrect: Context-aware typographical error correction via training data augmentation

Kshitij Shah, Gerard de Melo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
EditorsNicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
PublisherEuropean Language Resources Association (ELRA)
Pages6930-6936
Number of pages7
ISBN (Electronic)9791095546344
StatePublished - 2020
Event12th International Conference on Language Resources and Evaluation, LREC 2020 - Marseille, France
Duration: May 11 2020May 16 2020

Publication series

NameLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings

Conference

Conference12th International Conference on Language Resources and Evaluation, LREC 2020
Country/TerritoryFrance
CityMarseille
Period5/11/205/16/20

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Education
  • Library and Information Sciences
  • Linguistics and Language

Keywords

  • Corpus
  • Deep Learning
  • Error Generation

Fingerprint

Dive into the research topics of 'Correcting the autocorrect: Context-aware typographical error correction via training data augmentation'. Together they form a unique fingerprint.

Cite this