@inproceedings{al-sharou-etal-2021-towards,
    title = "Towards a Better Understanding of Noise in Natural Language Processing",
    author = "Al Sharou, Khetam  and
      Li, Zhenhao  and
      Specia, Lucia",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
    month = sep,
    year = "2021",
    address = "Held Online",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.ranlp-1.7/",
    pages = "53--62",
    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."
}