Regular Expressions for Fast-response COVID-19 Text Classification

• URL: http://arxiv.org/abs/2102.09507v2
• Date: Fri, 19 Feb 2021 19:23:33 GMT
• Title: Regular Expressions for Fast-response COVID-19 Text Classification
• Authors: Igor L. Markov, Jacqueline Liu, Adam Vagner
• Abstract summary: Facebook determines if a piece of text belongs to a narrow topic such as COVID-19. We employ human-guided iterations of keyword discovery, but do not require labeled data. Regular expressions enable low-latency queries from multiple platforms.
• Score: 1.1279808969568252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text classifiers are at the core of many NLP applications and use a variety of algorithmic approaches and software. This paper describes 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.

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