Improved Iterative Correction for Distant Spelling Errors

Gubanov, Sergey; Galinskaya, Irina; Baytin, Alexey

doi:10.3115/v1/p14-2028

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…We also observe that iterative correction, i.e. running the speller a second time over already corrected data, further improves performance slightly which is in-line with findings in (Cucerzan and Brill, 2004) and (Gubanov et al, 2014).…”

Section: Hillclimbing and Performancesupporting

confidence: 90%

Spelling Correction of User Search Queries through Statistical Machine Translation

Hasan¹,

Heger²,

Mansour³

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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We use character-based statistical machine translation in order to correct user search queries in the e-commerce domain. The training data is automatically extracted from event logs where users re-issue their search queries with potentially corrected spelling within the same session. We show results on a test set which was annotated by humans and compare against online autocorrection capabilities of three additional web sites. Overall, the methods presented in this paper outperform fully productized spellchecking and autocorrection services in terms of accuracy and F1 score. We also propose novel evaluation steps based on retrieved search results of the corrected queries in terms of quantity and relevance.

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Section: Hillclimbing and Performancesupporting

confidence: 90%

Spelling Correction of User Search Queries through Statistical Machine Translation

Hasan¹,

Heger²,

Mansour³

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…Farra et al (2014) suggest a context-sensitive characterlevel spelling error correction model. Gubanov et al (2014) improve the Cucerzan and Brill (2004) model by iterating the application of the basic noisy channel model for spelling correction in a stochastic manner.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Four Character-Level String-to-String Translation Models for (OCR) Spelling Error Correction

Eger¹,

Brück²,

Mehler³

2016

The Prague Bulletin of Mathematical Linguistics

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We consider the isolated spelling error correction problem as a specific subproblem of the more general string-to-string translation problem. In this context, we investigate four general string-to-string transformation models that have been suggested in recent years and apply them within the spelling error correction paradigm. In particular, we investigate how a simple ‘k-best decoding plus dictionary lookup’ strategy performs in this context and find that such an approach can significantly outdo baselines such as edit distance, weighted edit distance, and the noisy channel Brill and Moore model to spelling error correction. We also consider elementary combination techniques for our models such as language model weighted majority voting and center string combination. Finally, we consider real-world OCR post-correction for a dataset sampled from medieval Latin texts.

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“…Automatic spelling correction in non-malicious settings has had a long research track as an important component of text processing and normalization. Early works have used edit distance to find morphologically similar corrections (Ristad and Yianilos, 1998), noisy channel model for misspellings (Jurafsky and Martin, 2014), and iterative search to improve corrections of distant spelling errors (Gubanov et al, 2014). Word contexts have been shown to be improve the robustness of spell checkers with ngram language model as one approach to incorporate contextual information (Hassan and Menezes, 2013;Farra et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Context-Sensitive Malicious Spelling Error Correction

Gong

Bhat

et al. 2019

The World Wide Web Conference

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Misspelled words of the malicious kind work by changing specific keywords and are intended to thwart existing automated applications for cyber-environment control such as harassing content detection on the Internet and email spam detection. In this paper, we focus on malicious spelling correction, which requires an approach that relies on the context and the surface forms of targeted keywords. In the context of two applications-profanity detection and email spam detection-we show that malicious misspellings seriously degrade their performance. We then propose a contextsensitive approach for malicious spelling correction using word embeddings and demonstrate its superior performance compared to state-of-the-art spell checkers.

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Improved Iterative Correction for Distant Spelling Errors

Cited by 5 publications

References 7 publications

Spelling Correction of User Search Queries through Statistical Machine Translation

Spelling Correction of User Search Queries through Statistical Machine Translation

A Comparison of Four Character-Level String-to-String Translation Models for (OCR) Spelling Error Correction

Context-Sensitive Malicious Spelling Error Correction

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