Using NLP to Explore Entity Relationships in COVID-19 Literature

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In this talk, we will cover how to extract entities from text using both rule-based and deep learning techniques. We will also cover how to use rule-based entity extraction to bootstrap a named entity recognition model. The other important aspect of this project we will cover is how to infer relationships between entities, and combine them with explicit relationships found in the source data sets. Although this talk is focused on the CORD-19 data set, the techniques covered are applicable to a wide variety of domains. This talk is for those who want to learn how to use NLP to explore relationships in text.

What you will learn
– How to extract named entities without a model
– How to bootstrap an NLP model from rule-based techniques
– How to identify relationships between entities in text.

Speakers: Alexander Thomas and Vishnu Vettrivel


 
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About Alexander Thomas

Wisecube AI

Alex Thomas is a principal data scientist at Wisecube. He's used natural language processing and machine learning with clinical data, identity data, employer and jobseeker data, and now biochemical data. Alex is also the author of Natural Language Processing with Spark NLP.

About Vishnu Vettrivel

Wisecube AI

Vishnu Vettrivel is the Founder and CTO of Wisecube, a startup focused on accelerating biomedical research using AI. He has decades of experience building Data platforms and teams in healthcare, financial services and digital marketing.