2018 AutomaticSpellingCorrectionforR

• (Etoori et al., 2018) ⇒ Pravallika Etoori, Manoj Chinnakotla, and Radhika Mamidi. (2018). “Automatic Spelling Correction for Resource-Scarce Languages Using Deep Learning.” In: Proceedings of ACL 2018, Student Research Workshop.

Subject Headings: Spelling Error Correction System; Character-based Sequence-to-Sequence Text Correction Model for Indic Languages (SCMIL), Deep Learning System, Etoori-Chinnakotla-Mamidi Sequence-to-Sequence Spelling Error Correction System.

Cited By

• Google Scholar: ~ 7 Citations.
• (Melli et al., 2020) ⇒ Gabor Melli, Abdelrhman Eldallal, Bassim Lazem, and Olga Moreira. (2020). “GM-RKB WikiText Error Correction Task and Baselines.”. In: Proceedings of LREC 2020 (LREC-2020).

Quotes

Abstract

Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resource-scarce and do not have such parallel data due to low volume of queries and non-existence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resource-scarce languages. We propose a sequence-to-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 AutomaticSpellingCorrectionforR	Pravallika Etoori Manoj Chinnakotla Radhika Mamidi			Automatic Spelling Correction for Resource-Scarce Languages Using Deep Learning