Charting practice

In this exercise, you'll use some extracted named entities and their groupings from a series of newspaper articles to chart the diversity of named entity types in the articles.

You'll use a defaultdict called ner_categories, with keys representing every named entity group type, and values to count the number of each different named entity type. You have a chunked sentence list called chunked_sentences similar to the last exercise, but this time with non-binary category names.

You can use hasattr() to determine if each chunk has a 'label' and then simply use the chunk's .label() method as the dictionary key.

This exercise is part of the course

Introduction to Natural Language Processing in Python

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create the defaultdict: ner_categories
ner_categories = ____