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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.

Questo esercizio fa parte del corso

Introduction to Natural Language Processing in Python

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create the defaultdict: ner_categories
ner_categories = ____
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