Counting nouns in a piece of text
In this exercise, we will write two functions, nouns() and proper_nouns() that will count the number of other nouns and proper nouns in a piece of text respectively.
These functions will take in a piece of text and generate a list containing the POS tags for each word. It will then return the number of proper nouns/other nouns that the text contains. We will use these functions in the next exercise to generate interesting insights about fake news.
The en_core_web_sm model has already been loaded as nlp in this exercise.
Diese Übung ist Teil des Kurses
Feature Engineering for NLP in Python
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
nlp = spacy.load('en_core_web_sm')
# Returns number of proper nouns
def proper_nouns(text, model=nlp):
# Create doc object
doc = model(text)
# Generate list of POS tags
pos = [token.pos_ for token in doc]
# Return number of proper nouns
return ____.____(____)
print(proper_nouns("Abdul, Bill and Cathy went to the market to buy apples.", nlp))