COVID-Q

COVID-Q是由达特茅斯学院的研究团队创建的一个包含1,690个关于COVID-19问题的数据集。该数据集从13个不同来源收集问题，并将其分类为15个问题类别和207个问题集群。数据集主要关注COVID-19的传播、预防和社会影响等问题。创建过程中，研究团队通过网络爬虫从官方和非官方来源收集问题，并进行了严格的预处理和标注。COVID-Q旨在帮助开发和评估针对COVID-19相关问题的自然语言处理模型，特别是在问题分类和集群分析方面。

COVID-Q is a dataset containing 1,690 COVID-19-related questions, developed by a research team at Dartmouth College. The dataset collects questions from 13 distinct sources, and categorizes them into 15 question categories and 207 question clusters. It primarily focuses on issues related to COVID-19 transmission, prevention, and societal impacts. During its development, the research team collected questions from both official and unofficial sources via web crawling, and performed rigorous preprocessing and annotation. COVID-Q aims to assist in the development and evaluation of natural language processing models for COVID-19-related questions, particularly in the areas of question classification and cluster analysis.

搜集汇总

背景与挑战

背景概述

COVID-Q是由达特茅斯学院研究团队创建的COVID-19问题数据集，包含1,690个问题，从13个来源收集并分类为15个类别和207个集群，主要关注传播、预防和社会影响。该数据集通过严格预处理和标注，旨在支持开发和评估自然语言处理模型，特别是在问题分类和集群分析方面。