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        <title>Towards a Better Understanding of Noise in Natural Language Processing</title>
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        <namePart type="given">Khetam</namePart>
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    <abstract>In this paper, we propose a definition and taxonomy of various types of non-standard textual content – generally referred to as “noise” – in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially when dealing with user-generated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected. We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks. Our main goal is to raise awareness of non-standard content – which should not always be considered as “noise” – and of the need for careful, task-dependent pre-processing. This is an alternative to blanket, all-encompassing solutions generally applied by researchers through “standard” pre-processing pipelines. The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace non-standard content.</abstract>
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%0 Conference Proceedings
%T Towards a Better Understanding of Noise in Natural Language Processing
%A Al Sharou, Khetam
%A Li, Zhenhao
%A Specia, Lucia
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F al-sharou-etal-2021-towards
%X In this paper, we propose a definition and taxonomy of various types of non-standard textual content – generally referred to as “noise” – in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially when dealing with user-generated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected. We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks. Our main goal is to raise awareness of non-standard content – which should not always be considered as “noise” – and of the need for careful, task-dependent pre-processing. This is an alternative to blanket, all-encompassing solutions generally applied by researchers through “standard” pre-processing pipelines. The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace non-standard content.
%U https://aclanthology.org/2021.ranlp-1.7
%P 53-62

Markdown (Informal)

[Towards a Better Understanding of Noise in Natural Language Processing](https://aclanthology.org/2021.ranlp-1.7) (Al Sharou et al., RANLP 2021)

• Towards a Better Understanding of Noise in Natural Language Processing (Al Sharou et al., RANLP 2021)

ACL

• Khetam Al Sharou, Zhenhao Li, and Lucia Specia. 2021. Towards a Better Understanding of Noise in Natural Language Processing. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 53–62, Held Online. INCOMA Ltd..