text: 332,170,1268906731003957248,237918251,Wei Xu,"Our new work on automatically extracting COVID-19 events from Twitter. We annotated 7,500 tweets annotated with event QA/slot-filling information. (e.g., Who tested positive? Who is promoting a cure for coronavirus?) #nlproc Paper: <LINK> <LINK> @srchvrs Yes, these are simple baselines. BERT is not trained on Twitter data. So a lot of room for people to explore. Exact span match is also tricky. @srchvrs There are a lot of nominals and nested spans in the dataset. I would expect larger training data will improve F1 quite a bit, having better Twitter-based BERT may help some too. <LINK> @alvations We probably can’t share the user data. We are only going to release the Tweet IDs and event annotations. @yanaiela @yoavgo We tried BERTweet. It doesn’t help much, but it was trained on tweets preprocessed by NLTK TweeTokenizer before BPE. We also tried <LINK>, which was trained on smaller amount of tweets. @srchvrs We tried this one. It doesn’t help much, but it was trained on tweets preprocessed by NLTK TweeTokenizer before BPE. We also tried <LINK>, which was trained on smaller amount of tweets. I don’t think we can rule out Twitter-specific BERT would not help just yet. @srchvrs The F-scores reported in the paper are on extract span matches. So it’s kind of an overly strict metric for this task. @yoavgo Missed ""close_contact"" for tested positive: ""Jazz star Donovan Mitchell has tested positive for coronavirus, league sources tell ESPN. Jazz players privately say that &lt;E&gt; Rudy Gobert &lt;/E&gt; had been careless and borderline caviler in the locker room touching other players ..."" @yoavgo Over-prediction of the ""employer"" for tested positive: ""&lt;E&gt; Lee Health &lt;/E&gt; confirmed Friday night that a patient who tested positive for Covid-19 after arriving at Gulf Coast Medical Center later died."" @yoavgo Mismatched span for the ""employer"" for tested positive: ""Brandon from Crown the &lt;E&gt; Empire &lt;/E&gt; tested positive for Coronavirus"" (Crown the Empire is a band.) @yoavgo sounds interesting. haven't tried this.",https://arxiv.org/abs/2006.02567,"In this paper, we present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. We designed slot-filling questions for each event type and annotated a total of 31 fine-grained slots, such as the location of events, recent travel, and close contacts. We show that our corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events and help track the spread of a new disease. We also demonstrate that, by aggregating events extracted from millions of tweets, we achieve surprisingly high precision when answering complex queries, such as ""Which organizations have employees that tested positive in Philadelphia?"" We will release our corpus (with user-information removed), automatic extraction models, and the corresponding knowledge base to the research community. ",Extracting a Knowledge Base of COVID-19 Events from Social Media,11,"['Our new work on automatically extracting COVID-19 events from Twitter. We annotated 7,500 tweets annotated with event QA/slot-filling information. (e.g., Who tested positive? Who is promoting a cure for coronavirus?) #nlproc\n\nPaper: <LINK> <LINK>', '@srchvrs Yes, these are simple baselines. BERT is not trained on Twitter data. So a lot of room for people to explore. Exact span match is also tricky.', '@srchvrs There are a lot of nominals and nested spans in the dataset. I would expect larger training data will improve F1 quite a bit, having better Twitter-based BERT may help some too. https://t.co/MzL7PV2ksL', '@alvations We probably can’t share the user data. We are only going to release the Tweet IDs and event annotations.', '@yanaiela @yoavgo We tried BERTweet. It doesn’t help much, but it was trained on tweets preprocessed by NLTK TweeTokenizer before BPE. We also tried https://t.co/Iw7kBdxUJo, which was trained on smaller amount of tweets.', '@srchvrs We tried this one. It doesn’t help much, but it was trained on tweets preprocessed by NLTK TweeTokenizer before BPE. We also tried https://t.co/Iw7kBdxUJo, which was trained on smaller amount of tweets. I don’t think we can rule out Twitter-specific BERT would not help just yet.', '@srchvrs The F-scores reported in the paper are on extract span matches. So it’s kind of an overly strict metric for this task.', '@yoavgo Missed ""close_contact"" for tested positive:\n\n""Jazz star Donovan Mitchell has tested positive for coronavirus, league sources tell ESPN. Jazz players privately say that &lt;E&gt; Rudy Gobert &lt;/E&gt; had been careless and borderline caviler in the locker room touching other players ...""', '@yoavgo Over-prediction of the ""employer"" for tested positive:\n\n""&lt;E&gt; Lee Health &lt;/E&gt; confirmed Friday night that a patient who tested positive for Covid-19 after arriving at Gulf Coast Medical Center later died.""', '@yoavgo Mismatched span for the ""employer"" for tested positive:\n\n""Brandon from Crown the &lt;E&gt; Empire &lt;/E&gt; tested positive for Coronavirus""\n\n(Crown the Empire is a band.)', ""@yoavgo sounds interesting. haven't tried this.""]",20,06,2058