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Analyzing Biomedical and Clinical Text with the Stanza Python NLP Library

The growing interest in biomedical and clinical research has led to a wide need of analyzing and understanding text in these domains. While today’s open-source NLP tools have integrated sophisticated neural architectures that improve their performance on general-domain text, they often lack convenient support for the analysis of biomedical text at the same level of accuracy.

In this talk, I will talk about the out-of-the-box biomedical and clinical packages in the Stanza Python NLP toolkit.

I will start by talking about the fully neural architectural design of Stanza, which allows it to generalize with ease to over 70 languages and multiple domains.

Then I will talk about how we extend this design to build and evaluate the biomedical and clinical pipelines for Stanza, which provide near state-of-the-art performance for linguistic analysis and entity recognition tasks.

Lastly, I will showcase how these biomedical and clinical models can be used in common research and text analysis scenarios. You can try out an online demo of these packages at:

Training a Distributed NER model for drug, disease and condition identification

The recognition of drugs, diseases, and conditions from electronic medical records is a very important subtask in information extraction in clinical research...