Natural language understanding at scale with 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, 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, Claudiu Branzan, and Alex Thomas lead a hands-on tutorial for 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 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

• 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
• Creating custom pipelines

Using Spark ML, scikit-learn, or TensorFlow to build a domain-specific machine learning pipeline that includes and depends on NLP annotators to generate features

• Feature engineering and optimization
• Measurement
• 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
• Using pretrained models and pipelines
• 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

• Optical character recognition (OCR) annotators and pipelines
• Improving OCR accuracy with customized dictionaries, forms, and spell checkers
• Entity resolution versus named entity recognition
• Building automated knowledge graphs using entity resolution and curated ontologies
• An overview of state-of-the-art NLP algorithms and models for healthcare