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. \hush{PHP, Python, Java and SQL code} 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.

中文翻译：

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快速响应COVID-19文本分类的正则表达式

文本分类器是许多NLP应用程序的核心，并使用各种算法方法和软件。本文描述了Facebook如何确定给定的文本（从标签到帖子的任何内容）是否属于一个狭窄的主题，例如COVID-19。为了完全定义主题并评估分类器性能，我们采用了人工指导的关键字发现迭代，但不需要标记数据。对于COVID-19，我们构建了两组正则表达式：（1）用于66种语言，精度为99％，调用率> 50％；（2）用于11种最常见的语言，精度为> 90％，调用率> 90％ 。正则表达式支持从多个平台进行低延迟查询。\ hush {PHP，Python，Java和SQL代码}快速响应诸如COVID-19之类的挑战，修订版也是如此。与DNN分类器的比较显示出可以解释的结果，更高的精度和召回率，以及更少的过拟合。我们的学习可以应用于其他窄主题分类器。