Text analytics and spatial science to extract insights from unstructured text

Almost 80 percent of the world's data is unstructured, such as data found in emails, speech transcripts, briefings, social media posts, news reports, and more. Much of this unstructured data has a location variable tied to it (text origin or locations described within) and can be integrated into spatial analysis workflows such as analyzing police reports to understand a changing crime landscape or mining news articles to understand disease spread.

Topics covered include the following: 

  • Introduction to natural language processing (NLP)
  • Labeling and preparing training data
  • Extracting entities using the arcgis.learn EntityRecognizer module
  • Integration with third-party NLP frameworks
  • Demos and industry-specific use cases

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