Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Natural language understanding at scale with Spark NLP

Alexander Thomas (John Snow Labs), Claudiu Branzan (Accenture)
13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Average rating: ****.
(4.00, 4 ratings)

Who is this presentation for?

  • Data scientists and software developers



Prerequisite knowledge

  • Familiarity with machine learning, Python, and Spark

Materials or downloads needed in advance

  • A laptop (You'll be provided a ready-made Docker container that includes the full environment, datasets, and notebooks.)

What you'll learn

  • Learn how to build high-performance, high-accuracy NLP pipelines using Spark NLP, how to integrate them as part of machine learning and deep learning pipelines, and how to scale them in real-world systems


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, 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.

Claudiu Branzan and Alex Thomas lead a hands-on introduction to scalable 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 code base that you can change and improve.


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

  • When is an NLP library needed?
  • Introduction to Spark NLP
  • Benchmarks and scalability
  • Built-in Spark NLP annotators
  • Core NLP tasks: Tokenizer, normalizer, stemmer, lemmatizer, chunker, POS, and NER
  • Using pretrained models and pipelines

Building machine learning pipeline that includes and depends on NLP annotators to generate features:

  • Feature engineering and optimization
  • Trainable NLP tasks: Spell checker, sentiment analysis, named entity recognition
  • Applying word embeddings to “featurize” text
  • Best practices and common pitfalls for creating unified NLP and ML pipelines

Using Spark NLP with TensorFlow to train deep learning models for state-of-the-art NLP:

  • Why you’ll need to train domain-specific NLP models for most real-world use cases
  • Recent deep learning research results for named entity recognition, entity resolution, assertion status detection, and deidentification
  • Spark NLP and TensorFlow integration and benefits
  • Training your own domain-specific deep learning NLP models
  • Best practices for choosing between alternative NLP algorithms and annotators

Advanced Spark NLP functionality that enables a scalable open source solution to more complex language understanding use cases:

  • Spell checking and correction
  • Sentiment analysis and emotion detection
  • Optical character recognition (OCR) annotators and pipelines
  • Improving OCR accuracy with customized dictionaries, forms, and spell checkers
  • Entity resolution versus named entity recognition
  • An overview of state-of-the-art NLP algorithms and models for healthcare
Photo of Alexander Thomas

Alexander Thomas

John Snow Labs

Alex Thomas is a data scientist at John Snow Labs. He’s used natural language processing (NLP) and machine learning with clinical data, identity data, and job data. He’s worked with Apache Spark since version 0.9 as well as with NLP libraries and frameworks including UIMA and OpenNLP.

Photo of Claudiu Branzan

Claudiu Branzan


Claudiu Branzan is an analytics senior manager in the Applied Intelligence Group at Accenture, 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 using big data and advanced analytics to offer solutions for clients in healthcare, high-tech, telecom, and payments verticals.