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PyData Pune: Webinar 10 - Clinical Named Entity Recognition at Scale with Spark NLP

Spark NLP is a Natural Language Processing (NLP) library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that can scale easily in a distributed environment. Spark NLP comes with 1100+ pretrained pipelines and models in more than 192+ languages. It supports nearly all the NLP tasks and modules that can be used seam- lessly in a cluster. Downloaded more than 2.7 million times and experiencing 9x growth since January 2020, Spark NLP is used by 54% of healthcare organizations as the world’s most widely used NLP library in the enterprise.

Named entity recognition (NER) is one of the most important tasks for development of more sophisticated NLP systems and Spark NLP has an open source implementation of well know BiLSTM-CNN-Char architecture that can be used in a few lines of code. In this talk, we will walk you through how to prepare your own data and train a custom clinical NER model using BioBert and Glove embeddings in Spark NLP – taking advantage of transfer learning to greatly reduce the amount of annotated text to achieve accurate results.

About Veysel:

Veysel is a Principal Data Scientist at John Snow Labs in the USA, delivering hands-on Machine Learning projects in Healthcare and Life Sciences to top pharma companies such as Roche and Novartis. He is a seasoned data scientist with a strong background in every aspect of data science including machine learning, artificial intelligence and data science with over ten years of experience. He is a Google Developer Expert (GDE) in Machine Learning, featured by Google in various summits and events, and invited several mentorship programs initiated by Google. He was also a section leader and lecturer at Stanford University’s CS106A Programming Methodologies class during the summer of 2020.

Veysel has been pursuing his PhD in ML at Leiden University, Netherlands, delivering graduate level lectures in AutoML and Distributed Data Processing and published several research articles in peer-reviewed deep learning conferences. He has broad consulting experience on Statistics, Data Science, Software Architecture, DevOps, Machine Learning and AI to several start-ups, bootcamps and companies around the globe with various titles including CTO, Head of AI and Chief Data Scientist. He also speaks at Data Science & AI events, conferences and workshops, and has delivered more than 30 talks at International as well as national conferences and meetups.

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