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Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020最新文献

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Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media 荷兰社交媒体上民众对政府新冠疫情应对措施的看法
Pub Date : 2020-09-03 DOI: 10.18653/V1/2020.NLPCOVID19-2.17
Shihan Wang, M. Schraagen, E. T. K. Sang, M. Dastani
Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to July 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.
民意(公众表达的意见、态度或感受)是政府关心的因素,因为它直接影响政策的实施。考虑到新冠肺炎危机的空前性质,及时反映国民对政府措施和公告的情绪至关重要。在本文中,我们从2020年2月至7月从三个在线媒体来源(Twitter、Reddit和Nu.nl)收集的文本数据分析了荷兰公众对政府COVID-19措施的情绪。我们应用情绪分析方法来分析随时间推移的极性,并确定对两项具体的流行病政策的立场,即保持社交距离和戴口罩。提出的初步结果为了解大量社交媒体文本数据中显示的叙述提供了有价值的见解,有助于了解COVID-19措施对公众的影响。
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引用次数: 16
COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities COVID-19:基于语义的生物医学实体推荐管道
Pub Date : 2020-09-03 DOI: 10.18653/v1/2020.nlpcovid19-2.20
Márcia Barros, Andre Lamurias, Diana Sousa, Pedro Ruas, Francisco M. Couto
With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80%, whereas when using only one ontology, the results for precision@k drops to 20%, for the same users. Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.
随着关于COVID-19的出版物越来越多,提取适合每位研究人员的个性化知识是一项挑战。这项工作旨在建立一个新的基于语义的管道,为科学研究人员推荐生物医学实体。为此,我们开发了一个管道,该管道基于文档语料库上的命名实体识别(NER)创建隐式反馈矩阵,使用多学科本体来识别和链接实体。我们的假设是,通过在NER阶段使用来自不同领域的本体,我们可以改善应用于创建的数据集的最先进的协同过滤推荐系统的结果。使用COVID-19开放研究数据集(CORD-19)数据集进行的测试表明,例如,当使用四个本体时,precision@k的结果达到80%,而当仅使用一个本体时,对于相同的用户,precision@k的结果降至20%。此外,使用多字段实体可能有助于发现新项目,即使研究人员在他们的首选项集合中没有来自该字段的项目。
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引用次数: 4
Developing a Curated Topic Model for COVID-19 Medical Research Literature 建立COVID-19医学研究文献的策划主题模型
Pub Date : 2020-08-12 DOI: 10.18653/v1/2020.nlpcovid19-2.30
P. Resnik, K. Goodman, Mike Moran
Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.
主题模型可以促进大型文档集合中的搜索、导航和知识发现。但是,自动生成的主题模型会产生不能满足用户需求的结果。我们提倡在COVID-19文献的主题建模中建立一套以用户为中心的需求,并描述了正在进行的一项工作,即通过主题专业知识和医学研究人员参与医学文献的方式,为COVID-19文章开发一个策划主题模型。
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引用次数: 5
Characterizing drug mentions in COVID-19 Twitter Chatter 在COVID-19推特聊天中提到的药物特征
Pub Date : 2020-07-20 DOI: 10.18653/v1/2020.nlpcovid19-2.25
Ramya Tekumalla, J. Banda
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15% additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.
自2019冠状病毒病被列为全球大流行以来,人们进行了许多治疗和控制该病毒的尝试。虽然没有针对COVID-19的特定抗病毒治疗建议,但有几种药物可能有助于缓解症状。在这项工作中,我们挖掘了一个包含4.24亿条关于COVID-19的推文的大型推特数据集,以识别围绕药物提及的话语。虽然看起来是一个简单的任务,但由于Twitter中语言使用的非正式性质,我们证明了机器学习和传统自动化方法一起帮助完成这项任务的必要性。通过应用这些补充方法,我们能够恢复近15%的额外数据,使处理拼写错误成为处理社交媒体数据时需要的预处理步骤。
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引用次数: 9
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management cire - covid:面向新型冠状病毒学术信息管理的问答查询多文献汇总系统
Pub Date : 2020-05-04 DOI: 10.18653/v1/2020.nlpcovid19-2.14
Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham J. Barezi, Pascale Fung
The outbreak of COVID-19 raises attention from the researchers from various communities. While many scientific articles have been published, a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. To address the requests, we propose our CAiRE-COVID, a neural-based system that uses open-domain question answering (QA) techniques combined with summarization techniques for mining the available scientific literature. It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query. Fluent summaries are also provided to help understand the content in a more efficient way. Our system has been awarded as winner for one of the tasks in CORD-19 Kaggle Challenge. We also launched our CAiRE-COVID website for broader use. The code for our system is also open-sourced to bootstrap further study.
COVID-19的爆发引起了各界研究人员的关注。虽然已经发表了许多科学文章,但一个能够从最新学术资源中为COVID-19相关问题提供可靠信息的系统至关重要,特别是在当前时间紧迫的治疗患者和寻找治愈病毒的医学界。为了满足这些要求,我们提出了我们的CAiRE-COVID,这是一个基于神经的系统,它使用开放域问答(QA)技术结合摘要技术来挖掘可用的科学文献。它利用信息检索(IR)系统和QA模型从给定查询的现有文献中提取相关片段。还提供了流畅的摘要,以帮助更有效地理解内容。我们的系统在CORD-19 Kaggle挑战赛中获得了一个任务的冠军。我们还推出了aire - covid网站,以供更广泛使用。我们系统的代码也是开源的,以引导进一步的研究。
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引用次数: 75
Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case 跟踪和了解COVID-19期间的公众反应:以沙特阿拉伯为例
Pub Date : 1900-01-01 DOI: 10.18653/v1/2020.nlpcovid19-2.24
Aseel Addawood, Alhanouf Alsuwailem, Ali Alohali, D. Alajaji, Mashail Alturki, Jaida Alsuhaibani, Fawziah Aljabli
The coronavirus disease of 2019 (COVID-19) has had a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are being affected by these new measures. With restrictions like lockdowns and social distancing, it has become important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabian citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that originated in Saudi Arabia. Our results showed that people had maintained a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passed. Overall, the results showed that people were highly supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.
2019年冠状病毒病(COVID-19)对世界各地的经济和社会产生了巨大影响。虽然各国政府正在采取极端措施减少病毒的传播,但人们正在受到这些新措施的影响。在封锁和保持社交距离等限制措施下,了解公众对疫情的情绪反应变得非常重要。在本文中,我们研究了沙特阿拉伯公民对大流行的反应。我们利用了2020年期间发送的一系列阿拉伯语推文,主要是通过来自沙特阿拉伯的标签。我们的结果显示,人们对大流行保持了积极的反应。这种积极反应在新冠疫情爆发之初达到顶峰,随着时间的推移开始下降。总体而言,结果表明,在这场大流行中,人们相互高度支持。这项研究可以帮助研究人员和政策制定者了解大流行对社会的情感影响。
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引用次数: 12
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration 改进医学文献主题表示,辅助COVID-19文献检索
Pub Date : 1900-01-01 DOI: 10.18653/v1/2020.nlpcovid19-2.12
Yulia Otmakhova, K. Verspoor, Timothy Baldwin, Simon Suster, Jey Han Lau
Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and pro-pose several ways to improve topic coherence and specificity.
在COVID-19大流行的背景下,高效发现和探索生物医学文献变得越来越重要,基于主题的方法(如潜在狄利let分配(LDA))是实现这一目标的有用工具。在本研究中,我们比较了基于词标记的传统主题模型和基于医学概念的主题模型,并提出了几种提高主题一致性和专一性的方法。
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引用次数: 5
Explaining the Trump Gap in Social Distancing Using COVID Discourse 用COVID话语解释社交距离中的特朗普差距
Pub Date : 1900-01-01 DOI: 10.18653/v1/2020.nlpcovid19-2.10
Austin Van Loon, Sheridan A Stewart, Brandon Waldon, S. K. Lakshmikanth, Ishan Shah, Sharath Chandra Guntuku, G. Sherman, J. Zou, J. Eichstaedt
Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “Trump Gap,” or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaningmaking determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.
我们限制COVID-19未来传播的能力在一定程度上取决于我们对导致人们遵循或拒绝冠状病毒健康行为的心理和社会学过程的理解。我们认为,该病毒在美国各地的社区中具有不同的含义,这些不同的含义在美国疫情爆发的早期、关键阶段塑造了社区对病毒的反应。使用词嵌入,我们证明,居民平均社会距离较短(以居民流动性衡量)的县,在他们的COVID话语中,更多地在语义上将病毒与欺诈、政治左派、还有像流感这样的良性疾病。我们还表明,病毒在不同社区的不同含义解释了我们所谓的“特朗普差距”的很大一部分,或更多支持特朗普的国家减少社会距离的经验趋势。这项工作表明,社区层面的意义制定过程决定了对COVID-19大流行的行为反应,这些过程可以使用Twitter进行不显眼的测量。
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引用次数: 8
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Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
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