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 and Claudiu Branzan 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
You’ll cover the built-in spaCy annotators, debugging & visualizing results, creating custom pipelines, and practical trade-offs for large scale projects, as well as for balancing performance vs accuracy.
Using TensorFlow to build domain specific, machine-learned annotators and then integrating them into an existing NLP pipeline
You’ll explore feature engineering, optimization, measurement, and specific 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
You’ll compare existing implementations of word2vec and doc2vec, learn when they are useful, and see how they can be applied in practice to increase the accuracy of classification or information retrieval problems. You’ll also examine current trade-offs in integrating spaCy and Spark when engineering distributed, large-scale NLP pipelines.
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. Earlier, he 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 the director of data science at G2 Web Services, where he designs and implements data science solutions to mitigate merchant risk, leveraging his 10+ years of machine learning and distributed systems experience. Previously, Claudiu worked for Atigeo building big data and data science-driven products for various customers.
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