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
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create the defaultdict: ner_categories
ner_categories = ____