# Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

arXiv: https://arxiv.org/abs/2104.03848 · HTML: https://mlsanity.com/paper/2104.03848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Streaming Models for Joint Speech Recognition and Translation](https://mlsanity.com/paper/2101.09149/) (2021)
- [Simplify-then-Translate: Automatic Preprocessing for Black-Box Machine Translation](https://mlsanity.com/paper/2005.11197/) (2020)
- [Cross-referencing using Fine-grained Topic Modeling](https://mlsanity.com/paper/1905.07508/) (2019)

## Abstract (arXiv)

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.