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Tutorial Syllabus

The coding samples in the following notebooks help illustrate the use of pytextrank and related libraries in Python for graph-based natural language work. We'll show how to leverage the different Textgraph algorithms within spaCy pipelines.

Audience

  • You are a Python programmer who needs to learn how to use available NLP packages
  • You work on a data science team and have some Python experience, and now you need to leverage NLP and text mining
  • You have interests in chatbots, deep learning, and related AI work, and want to understand the basics for handling text data in those use cases

Prerequisites

  • Some coding experience in Python (you can read a 20-line program)
  • Interest in use cases that need to use natural language processing

Key Takeaways

  • Hands-on experience with popular open source libraries in Python for natural language
  • Coding examples that can be used as starting points for your own NLP projects
  • Ways to integration natural language work with other aspects of graph-based data science
Last update: 2021-03-17