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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.

About the speaker

Danilo Burbano
Software and Machine Learning Engineer

Danilo Burbano is a Software and Machine Learning Engineer at John Snow Labs. He holds an MSc in Computer Science and has 12 years of commercial experience.

He has previously developed several software solutions over distributed system environments like microservices and big data pipelines across different industries and countries. Danilo has contributed to Spark NLP for the last 4 years. He is now working to maintain and evolve the Spark NLP library by continuously adding state-of-the-art NLP tools to allow the community to implement and deploy cutting-edge large-scale projects for AI and NLP.