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Using Spark NLP to build a drug discovery knowledge graph for COVID-19

In this talk, we will cover how to extract entities from text using both rule-based and deep learning techniques, and build a knowledge graph of these entities. We will 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.

Benefits and Challenges in de-identifying and linking unstructured medical records

There are tremendous research benefits of linking de-identified patient records to get a holistic patient view especially for studies related to drug...