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.
Deze oefening maakt deel uit van de cursus
Introduction to Natural Language Processing in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Create the defaultdict: ner_categories
ner_categories = ____