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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 recognize entities of interest in large-scale environments when dealing with lots of documents from medical records, web pages, or data gathered from social media.
In this talk, we will see how to retrieve the information we are looking for by using the following annotators:

  • Entity Ruler, an annotator available in open-source Spark NLP.
  • Contextual Parser, an annotator available only in Spark NLP for Healthcare.
  • In addition, we will enumerate use cases where we can apply these annotators.

After this webinar, you will know when to use a rule approach to extract information from your data and the best way to set the available parameters in these annotators.

Serving Spark NLP via API (1/3): Microsoft’s Synapse ML

This is the first article of the “Serving Spark NLP via API” series, showcasing how to serve Spark NLP using Synapse ML...