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3 cards from this section. Quick, good the night before a test.
What is tokenization?
Think about how you can break a sentence into words.
Name one example of a stopword.
These are common words often removed in preprocessing.
What does tokenization achieve?
Think about the action done on the text.
True or False: Stopword removal only focuses on nouns.
Consider if stopwords include other types of words.
Given the text 'The quick brown fox jumps over the lazy dog', write Python code to tokenize, remove stopwords, and apply stemming.
Consider which libraries you need for each step in Python.
Explain how you would evaluate the effectiveness of your text preprocessing on a machine learning model.
Think about comparing model performance with and without preprocessing steps.
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