基于正则表达式的快速响应COVID-19文本分类
Regular Expressions for Fast-response COVID-19 Text Classification
摘要：
文本分类器是许多NLP应用程序的核心，使用各种算法方法和软件。本文介绍了基于大规模正则表达式的文本分类器的基础设施和方法。特别是，我们描述了Facebook如何确定一段给定的文本-从标签到帖子-是否属于一个狭窄的主题，如COVID-19。为了完全定义主题并评估分类器性能，我们采用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）针对66种语言，精确度> 99%，召回率> 50%;（2）针对11种最常见的语言，精确度>90%，召回率> 90%。正则表达式支持来自多个平台的低延迟查询。对COVID-19等挑战的反应很快，修订也很快。与DNN分类器的比较显示了可解释的结果，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
