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.- Anthology ID:
- 2021.naacl-main.244
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3069–3082
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.244
- DOI:
- 10.18653/v1/2021.naacl-main.244
- Cite (ACL):
- Wilson Fearn, Orion Weller, and Kevin Seppi. 2021. Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3069–3082, Online. Association for Computational Linguistics.
- Cite (Informal):
- Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification (Fearn et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.naacl-main.244.pdf
- Code
- wfearn/preprocessing-paper