February 11, 2020

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Natural language understanding at scale with Spark NLP

Natural language understanding at scale with Spark NLP

David Talby, Alex Thomas, Saif Addin Ellafi, and Claudiu Branzan walk you through state-of-the-art natural language processing (NLP) using the highly performant, highly scalable open source Spark NLP library. You'll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve.

Talk Title Natural language understanding at scale with Spark NLP
Speakers David Talby (Pacific AI), Alex Thomas (John Snow Labs), Saif Addin Ellafi (John Snow Labs), Claudiu Branzan (Accenture)
Conference Strata Data Conference
Conf Tag Make Data Work
Location New York, New York
Date September 24-26, 2019
URL Talk Page
Slides Talk Slides
Video

NLP is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, entity recognition, sentiment analysis, dependency parsing, de-identification, and natural language BI. Building such systems usually requires combining three types of software libraries: NLP annotation frameworks, machine learning frameworks, and deep learning frameworks. David Talby, Alex Thomas, Saif Addin Ellafi, and Claudiu Branzan walk you through state-of-the-art natural language processing (NLP) using the highly performant, highly scalable open source Spark NLP library. You’ll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve. Outline: Using Spark NLP to build an NLP pipeline that can understand text structure, grammar, and sentiment and perform entity recognition Building a machine learning pipeline that includes and depends on NLP annotators to generate features Using Spark NLP with TensorFlow to train deep learning models for state-of-the-art NLP Advanced Spark NLP functionality that enables a scalable open source solution to more complex language-understanding use cases

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