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Answering natural language questions

The ability to directly answer medical questions asked in natural language either about a single entity (“what drugs has this patient been prescribed?”) or a set of entities (“list stage 4 lung cancer patients with no history of smoking”) has been a longstanding industry goal, given its broad applicability across many use cases.

This webinar presents a software solution, based on state-of-the-art deep learning and transfer learning research, for translating natural language questions to SQL statements. An actual case study will be a system which answers clinical questions by training domain-specific models and learning from reference data. This is a production-grade, trainable and scalable capability of Spark NLP Enterprise. Live notebooks will be shared to explain how you can use it in your own projects.

State of the Art Natural Language Understanding in Python

This talk introduces Spark NLP: the most widely used NLP library in the enterprise, thanks to implementing production-grade, trainable, and scalable versions...