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State of the art Clinical Named Entity Recognition in Spark NLP | Workshop | Veysel Kocaman

Watch all NLP Summit 2020 sessions: https://www.nlpsummit.org/ Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks. However, these gains rely on the availability of large amounts of annotated examples, without which state-of-the-art performance is rarely achievable. This is especially inconvenient for the many NLP fields where annotated examples are scarce, such as medical text. Named entity recognition (NER) is one of the most important tasks for development of more sophisticated NLP systems. In this webinar, we will walk you through how to train a custom NER model using BERT embeddings in Spark NLP – taking advantage of transfer learning to greatly reduce the amount of annotated text to achieve accurate results. After the webinar, you will be able to train your own NER models with your own data in Spark NLP.

Get started with Machine Learning in Java using Spark NLP

It is increasingly common for software developers to require Machine Learning technology in their applications. While Python is the de facto standard...