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.
Using spaCy to build an NLP annotations pipeline that can understand text structure, grammar, and sentiment and perform entity recognition
Using TensorFlow to build domain-specific machine-learned annotators and then integrating them into an existing NLP pipeline
Using Spark ML and TensorFlow to apply deep learning to expand and update ontologies
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.
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.
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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