The notebooks in this directory go through an example market intelligence use case involving Text Extensions for Pandas, IBM Watson Natural Language Understanding, SpaCy, and Ray.

The use case is broken down as follows:

• Market_Intelligence_Part1.ipynb: Use Text Extensions for Pandas and Watson Natural Language Understanding to identify executives quoted by name in IBM press releases.
• Market_Intelligence_Part2.ipynb: Use Text Extensions for Pandas and the SpaCy dependency parser to extract the titles of the executives we found in part 1.

Steps to run the notebooks in this directory

1. Follow the instructions in the top level README file for setting up a Python environment for running Jupyter notebooks in this project.

2. Activate your Python environment and install the SpaCy dependency parser model (required for Part 2) by running the command:

   python -m spacy download en_core_web_trf
   

3. Create a free instance of Watson Natural Language Understanding by visiting this page and clicking on the "Get started free" button.

4. Set the following environment variables:

   • IBM_API_KEY: The API key for your Natural Language Understanding instance
   • IBM_SERVICE_URL: The service URL for your Natural Language Understanding instance

5. Type jupyter lab to start JupyterLab, navigate to this directory, and run the notebook of your choice.