@inproceedings{fearn-etal-2021-exploring,
    title = "Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification",
    author = "Fearn, Wilson  and
      Weller, Orion  and
      Seppi, Kevin",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.naacl-main.244/",
    doi = "10.18653/v1/2021.naacl-main.244",
    pages = "3069--3082",
    abstract = "Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus' vocabulary due to their bag-of-words representation. Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model{'}s run-time. To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5{\%} of the accuracy for up to a 65{\%} reduction of run-time. Furthermore, some combinations of preprocessing techniques can even provide a 15{\%} reduction in run-time while simultaneously improving model accuracy."
}