Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Natural language understanding at scale with spaCy and Spark NLP

David Talby (Pacific AI), Claudiu Branzan (Accenture AI), Alex Thomas (Indeed)
1:30pm5:00pm Tuesday, March 6, 2018
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data scientists

Prerequisite knowledge

  • A working knowledge of Python, Spark, and machine learning

Materials or downloads needed in advance

  • A laptop with the course Docker container, libraries, and notebooks downloaded.
  • To be able to run the materials for this tutorial, please follow the instructions from the "Installation Instructions.pdf" you can find at https://github.com/melcutz/NLU_tutorial
  • IMPORTANT: These instructions imply downloading and running a large Docker container on your laptop and we advise you to do this well in advance of the event.

What you'll learn

  • Gain hands-on experience with common NLP tasks and pipelines using spaCy and Spark NLP

Description

Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. Building such systems usually requires combining three types of software libraries: NLP annotation frameworks, machine learning frameworks, and deep learning frameworks.

David Talby, Claudiu Branzan, and Alex Thomas lead a hands-on tutorial for scalable NLP using spaCy for building annotation pipelines, Spark NLP for training distributed custom natural language machine-learned pipelines, and Spark ML and TensorFlow for using deep learning to build and apply word embeddings. You’ll spend about half your time coding as you work through three sections, each with an end-to-end working codebase that you are then asked to change and improve.

Outline

Using spaCy to build an NLP annotations pipeline that can understand text structure, grammar, and sentiment and perform entity recognition

  • Built-in spaCy annotators
  • Debugging and visualizing results
  • Creating custom pipelines
  • Practical trade-offs for large-scale projects, as well as for balancing performance and accuracy

Using TensorFlow to build domain-specific machine-learned annotators and then integrating them into an existing NLP pipeline

  • Feature engineering and optimization
  • Measurement
  • Practical considerations when working on problems that require understanding text beyond keyword matching and one-hot encoding

Using Spark ML and TensorFlow to apply deep learning to expand and update ontologies

  • Comparison of word2vec and doc2vec
  • When each is useful
  • How to apply them to increase the accuracy of classification or information retrieval problems
  • Current trade-offs in integrating spaCy and Spark when engineering distributed, large-scale NLP pipelines
Photo of David Talby

David Talby

Pacific AI

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe, and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Photo of Claudiu Branzan

Claudiu Branzan

Accenture AI

Claudiu Branzan is a analytics senior manager in Accenture’s Applied Intelligence Group, based in Seattle, where he leverages his more than 10 years of expertise in data science, machine learning, and AI to promote the use and benefits of these technologies to build smarter solutions to complex problems. Previously, Claudiu held highly technical client-facing leadership roles in companies utilizing big data and advanced analytics to offer solutions for clients in healthcare, high-tech, telecom, and payments verticals.

Photo of Alex Thomas

Alex Thomas

Indeed

Alex Thomas is a data scientist at Indeed. He’s used natural language processing (NLP) and machine learning with clinical data, identity data, and now employer and jobseeker data. An Apache Spark user since version 0.9, he’s also worked with NLP libraries and frameworks, including UIMA and OpenNLP.

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Picture of Sridhar Alla
Sridhar Alla | BIG DATA ARCHITECT
03/06/2018 6:34am PST

I setup an AWS instance for the lab.

18.219.0.106:8888/?token=1e119ad4ea1671e1bde4a489ac9b6d44ec0cf9fa4892c524