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Spark NLP Blog

State of the Art Natural Language Processing at Scale.

PICO Classification – Using Spark NLP to improve PICO Element Identification

  The proliferation of healthcare data has contributed to the widespread usage of the PICO paradigm for creating specific clinical questions from RCT. PICO is a mnemonic that stands for:...

Serving Spark NLP via API: Spring and LightPipelines

Welcome to a follow-up article on the “Serving Spark NLP via API” series, showcasing how to serve SparkNLP using Spring, Swagger, and Java. Don’t forget to check the other articles...

Randomized Controlled Trials (RCT) classification using Spark NLP

In this article, I give a brief introduction to Randomized Controlled Trials (RCT). Also, an overview of the classification models and pretrained pipelines available in Spark NLP for the classification...

John Snow Labs Releases Spark NLP 4.0, Delivering 8x Speedups, Native M1 Support, and 1,000+ New Models to the Most Used NLP Library in the Enterprise

Modern Extractive Question Answering Annotators, Notable Performance Improvements, and State-of-the-Art Models Define Spark NLP 4.0...

Rule Based and Pattern Matching for Entity Recognition in Spark NLP

Finding patterns and matching strategies are well-known NLP procedures to extract information from text. Spark NLP library has two annotators that can use these techniques to extract relevant information or...