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Misspellings in natural language processing: A survey of recent literature.

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Title: Misspellings in natural language processing: A survey of recent literature.
Source: Natural Language Processing (29770424). Mar2026, Vol. 32 Issue 2, p1-47. 47p.
Subjects: Spelling errors, Natural language processing, Machine translating, Language models, Data augmentation
Abstract: This survey provides an overview of the challenges of misspellings in natural language processing (NLP). Misspellings are ubiquitous in digital communication, and even if humans can generally interpret misspelt text, NLP models frequently struggle to handle it: this causes a decline in performance in common tasks like text classification and machine translation. In this paper, we reconstruct a history of misspellings as a scientific problem. We then discuss the latest advancements to address the challenge of misspellings in NLP. Main strategies to mitigate the effect of misspellings include data augmentation, double step, character-order agnostic, and tuple-based methods, among others. This survey also examines dedicated data challenges and competitions to spur progress in the field. Critical safety and ethical concerns are also examined, for example, the voluntary use of misspellings to inject malicious messages and hate speech on social networks. The survey also explores psycholinguistic perspectives on how humans process misspellings, potentially informing innovative computational techniques for text normalisation and representation. Additionally, the survey explores the challenges that misspellings pose in multilingual contexts. Finally, the misspelling-related challenges and opportunities associated with modern large language models are also analysed, including benchmarks, datasets and performances of the most prominent language models against misspellings. This survey provides a comprehensive review of recent research on misspellings and aims to serve as a valuable resource for researchers seeking to get up to speed on this problem within the rapidly evolving landscape of NLP. [ABSTRACT FROM AUTHOR]
Copyright of Natural Language Processing (29770424) is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 193314944
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Misspellings in natural language processing: A survey of recent literature.
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  Data: <searchLink fieldCode="DE" term="%22Spelling+errors%22">Spelling errors</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+translating%22">Machine translating</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink>
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  Data: This survey provides an overview of the challenges of misspellings in natural language processing (NLP). Misspellings are ubiquitous in digital communication, and even if humans can generally interpret misspelt text, NLP models frequently struggle to handle it: this causes a decline in performance in common tasks like text classification and machine translation. In this paper, we reconstruct a history of misspellings as a scientific problem. We then discuss the latest advancements to address the challenge of misspellings in NLP. Main strategies to mitigate the effect of misspellings include data augmentation, double step, character-order agnostic, and tuple-based methods, among others. This survey also examines dedicated data challenges and competitions to spur progress in the field. Critical safety and ethical concerns are also examined, for example, the voluntary use of misspellings to inject malicious messages and hate speech on social networks. The survey also explores psycholinguistic perspectives on how humans process misspellings, potentially informing innovative computational techniques for text normalisation and representation. Additionally, the survey explores the challenges that misspellings pose in multilingual contexts. Finally, the misspelling-related challenges and opportunities associated with modern large language models are also analysed, including benchmarks, datasets and performances of the most prominent language models against misspellings. This survey provides a comprehensive review of recent research on misspellings and aims to serve as a valuable resource for researchers seeking to get up to speed on this problem within the rapidly evolving landscape of NLP. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Natural Language Processing (29770424) is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1017/nlp.2026.10020
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      – Code: eng
        Text: English
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        PageCount: 47
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    Subjects:
      – SubjectFull: Spelling errors
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Machine translating
        Type: general
      – SubjectFull: Language models
        Type: general
      – SubjectFull: Data augmentation
        Type: general
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      – TitleFull: Misspellings in natural language processing: A survey of recent literature.
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              Text: Mar2026
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              Y: 2026
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