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 prepare your own data and train a custom clinical NER model using Bert and Glove 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.

Instructor's Bio

Veysel Kocaman, Lead Data Scientist and ML Engineer at John Snow Labs

Veysel Kocaman is a Lead Data Scientist and ML Engineer at John Snow Labs and has a decade long industry experience. He is also pursuing his Ph.D. in CS as well as giving lectures at Leiden University (NL) and several other platforms. He holds an MS degree in Operations Research from Penn State University and is affiliated with Google as a Developer Expert in Machine Learning.

Local ODSC chapter in Copenhagen, Denmark


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    State-of-the-art Named Entity Recognition in Spark NLP

    • Recording