natural-language-processing-2

Extracting COVID-19 Events from Twitter

3260 papers • 126 benchmarks • 313 datasets

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W-NUT 2020 Shared Task-3

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MOS Dataset

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Most implemented papers

Extracting a Knowledge Base of COVID-19 Events from Social Media

Alan Ritter, Wei Xu, Shi Zong, Ashutosh Baheti•Tue Jun 02 2020

A manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events is presented, showing that the corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events, which can be further collected for building a knowledge base.

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Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets

Ayush Kaushal, Tejas Vaidhya•Sat Oct 31 2020

This paper describes the system entry for WNUT 2020 Shared Task-3, aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection.

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