# Regular Expressions for Fast-response COVID-19 Text Classification

arXiv: https://arxiv.org/abs/2102.09507 · HTML: https://mlsanity.com/paper/2102.09507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge](https://mlsanity.com/paper/1608.03000/) (2016)
- [Preserving Integrity in Online Social Networks](https://mlsanity.com/paper/2009.10311/) (2020)
- [Multi-modal Synthesis of Regular Expressions](https://mlsanity.com/paper/1908.03316/) (2019)
- [Sketch-Driven Regular Expression Generation from Natural Language and Examples](https://mlsanity.com/paper/1908.05848/) (2019)
- [Revisiting Regex Generation for Modeling Industrial Applications by Incorporating Byte Pair Encoder](https://mlsanity.com/paper/2005.02558/) (2020)

## Abstract (arXiv)

Text classifiers are at the core of many NLP applications and use a variety of algorithmic approaches and software. This paper introduces infrastructure and methodologies for text classifiers based on large-scale regular expressions. In particular, we describe how Facebook determines if a given piece of text - anything from a hashtag to a post - belongs to a narrow topic such as COVID-19. To fully define a topic and evaluate classifier performance we employ human-guided iterations of keyword discovery, but do not require labeled data. For COVID-19, we build two sets of regular expressions: (1) for 66 languages, with 99% precision and recall >50%, (2) for the 11 most common languages, with precision >90% and recall >90%. Regular expressions enable low-latency queries from multiple platforms. Response to challenges like COVID-19 is fast and so are revisions. Comparisons to a DNN classifier show explainable results, higher precision and recall, and less overfitting. Our learnings can be applied to other narrow-topic classifiers.