用于SARS-CoV-2与COVID-19相关生物医学自然语言处理的英语词典、金标准和银标准语料库
English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19
AI总结：
针对COVID-19相关文本量大、术语变体多的NLP处理难题，研究团队发布包含同义词词典、金/银标准语料库及标注代码的工具包，免费开源，可支持多种新冠相关文本分析任务且已投入多项研究应用。
摘要：
由于COVID-19相关的出版物、报告和社交媒体帖子数量庞大，远超人类处理能力，因此需要借助自然语言处理（NLP）工具进行自动化信息抽取，以获取系统性的洞见。NLP面临的一大关键挑战是描述医疗实体的术语存在大量变体，这一问题在这种新发疾病中尤为突出。本文提出了一个NLP工具包，其中包含规模极大的SARS-CoV-2（含变异株名称）和COVID-19英语同义词词典，可适配基于词典的NLP工具。我们还发布了利用上述词典生成的银标准语料库，以及由PubMed摘要构成的金标准语料库——后者经过人工标注，涵盖疾病、病毒、症状、蛋白质/基因、细胞类型、化学物质和物种术语，可用于训练和评估COVID-19相关NLP工具。工具包中还包含标注代码，可用于扩展银标准语料库或开展文本挖掘工作。该工具包在GitHub（网址为https://github.com/Aitslab/corona）和zenodo（网址为https://doi.org/10.5281/zenodo.6642275）上免费开放获取。该工具包可用于处理与COVID-19危机相关的各类文本分析任务，目前已被用于构建COVID-19知识图谱、研究COVID-19相关术语的变异性与演化，以及开发和基准测试文本挖掘工具。
英文摘要：
Automated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. A key challenge for NLP is the extensive variation in terminology used to describe medical entities, which was especially pronounced for this newly emergent disease. Here we present an NLP toolbox comprising very large English dictionaries of synonyms for SARS-CoV-2 (including variant names) and COVID-19, which can be used with dictionary-based NLP tools. We also present a silver standard corpus generated with the dictionaries, and a gold standard corpus, consisting of PubMed abstracts manually annotated for disease, virus, symptom, protein/gene, cell type, chemical and species terms, which can be used to train and evaluate COVID-19-related NLP tools. Code for annotation, which can be used to expand the silver standard corpus or for text mining is also included. This toolbox is freely available on GitHub (on https://github.com/Aitslab/corona) and zenodo (https://doi.org/10.5281/zenodo.6642275). The toolbox can be used for a variety of text analytics tasks related to the COVID-19 crisis and has already been used to create a COVID-19 knowledge graph, study the variability and evolution of COVID-19-related terminology and develop and benchmark text mining tools.
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。
