12:00 Introduction & Welcome
12:05 Talk #1 NLP 4 COVID: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles
12:30 Talk #2 TBA
𝗧𝗮𝗹𝗸 # 𝟭 𝗯𝘆 𝗭𝘂𝗯𝗮𝗶𝗿 𝗔𝗳𝘇𝗮𝗹
Zubair Afzal is a Principal NLP Scientist at Elsevier. He has a PhD in Clinical Text Mining and a Professional Doctorate in Engineering. He has been working in Elsevier for over 5 years. His main interests are in the fields of NLP and NLU using deep learning methods.
Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world, have joined forces in an unprecedented manner to fight the threat. In this combat against the virus responsible for the pandemic, key for any advancements is the timely, accurate, peer-reviewed, and efficient communication of any novel research findings. They present a novel framework to address the information need of filtering efficiently the scientific bibliography for relevant literature around COVID-19. The contributions of their work are summarized in the following: they define and describe the information need that encompasses the major requirements for COVID-19 articles’ relevancy, we present and release an expert-curated benchmark set for the task, and we introduce CORA (Covid Relevancy Algorithm), a deep active learning model to identify core scientific articles.
𝗧𝗮𝗹𝗸 # 𝟮 𝗯𝘆 𝗧𝗕𝗔
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