Identifying and analyzing ‘noisy’ spelling errors in a second language corpus

Alan Juffs; Ben Naismith

doi:10.18653/v1/2025.wnut-1.4

Identifying and analyzing ‘noisy’ spelling errors in a second language corpus

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Abstract

This paper addresses the problem of identifying and analyzing ‘noisy’ spelling errors in texts written by second language (L2) learners’ texts in a written corpus. Using Python, spelling errors were identified in 5774 texts greater than or equal to 66 words (total=1,814,209 words), selected from a corpus of 4.2 million words (Authors-1). The statistical analysis used hurdle() models in R, which are appropriate for non-normal, count data, with many zeros.

Anthology ID:: 2025.wnut-1.4
Volume:: Proceedings of the Tenth Workshop on Noisy and User-generated Text
Month:: May
Year:: 2025
Address:: Albuquerque, New Mexico, USA
Editors:: JinYeong Bak, Rob van der Goot, Hyeju Jang, Weerayut Buaphet, Alan Ramponi, Wei Xu, Alan Ritter
Venues:: WNUT | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 26–37
Language:
URL:: https://aclanthology.org/2025.wnut-1.4/
DOI:: 10.18653/v1/2025.wnut-1.4
Bibkey:
Cite (ACL):: Alan Juffs and Ben Naismith. 2025. Identifying and analyzing ‘noisy’ spelling errors in a second language corpus. In Proceedings of the Tenth Workshop on Noisy and User-generated Text, pages 26–37, Albuquerque, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):: Identifying and analyzing ‘noisy’ spelling errors in a second language corpus (Juffs & Naismith, WNUT 2025)
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PDF:: https://aclanthology.org/2025.wnut-1.4.pdf

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@inproceedings{juffs-naismith-2025-identifying,
    title = "Identifying and analyzing `noisy' spelling errors in a second language corpus",
    author = "Juffs, Alan  and
      Naismith, Ben",
    editor = "Bak, JinYeong  and
      Goot, Rob van der  and
      Jang, Hyeju  and
      Buaphet, Weerayut  and
      Ramponi, Alan  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the Tenth Workshop on Noisy and User-generated Text",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.wnut-1.4/",
    doi = "10.18653/v1/2025.wnut-1.4",
    pages = "26--37",
    ISBN = "979-8-89176-232-9",
    abstract = "This paper addresses the problem of identifying and analyzing `noisy' spelling errors in texts written by second language (L2) learners' texts in a written corpus. Using Python, spelling errors were identified in 5774 texts greater than or equal to 66 words (total=1,814,209 words), selected from a corpus of 4.2 million words (Authors-1). The statistical analysis used hurdle() models in R, which are appropriate for non-normal, count data, with many zeros."
}

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%0 Conference Proceedings
%T Identifying and analyzing ‘noisy’ spelling errors in a second language corpus
%A Juffs, Alan
%A Naismith, Ben
%Y Bak, JinYeong
%Y Goot, Rob van der
%Y Jang, Hyeju
%Y Buaphet, Weerayut
%Y Ramponi, Alan
%Y Xu, Wei
%Y Ritter, Alan
%S Proceedings of the Tenth Workshop on Noisy and User-generated Text
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-232-9
%F juffs-naismith-2025-identifying
%X This paper addresses the problem of identifying and analyzing ‘noisy’ spelling errors in texts written by second language (L2) learners’ texts in a written corpus. Using Python, spelling errors were identified in 5774 texts greater than or equal to 66 words (total=1,814,209 words), selected from a corpus of 4.2 million words (Authors-1). The statistical analysis used hurdle() models in R, which are appropriate for non-normal, count data, with many zeros.
%R 10.18653/v1/2025.wnut-1.4
%U https://aclanthology.org/2025.wnut-1.4/
%U https://doi.org/10.18653/v1/2025.wnut-1.4
%P 26-37

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Markdown (Informal)

[Identifying and analyzing ‘noisy’ spelling errors in a second language corpus](https://aclanthology.org/2025.wnut-1.4/) (Juffs & Naismith, WNUT 2025)

Identifying and analyzing ‘noisy’ spelling errors in a second language corpus (Juffs & Naismith, WNUT 2025)

ACL

Alan Juffs and Ben Naismith. 2025. Identifying and analyzing ‘noisy’ spelling errors in a second language corpus. In Proceedings of the Tenth Workshop on Noisy and User-generated Text, pages 26–37, Albuquerque, New Mexico, USA. Association for Computational Linguistics.