Practice Text Preprocessing - 9.2.1 | 9. Natural Language Processing (NLP) | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is tokenization?

💡 Hint: What do we do to make text easier to analyze?

Question 2

Easy

Give an example of a stop-word.

💡 Hint: What are some common words that we often remove in analysis?

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does tokenization accomplish?

  • Reduces words to their root
  • Splits text into smaller components
  • Filters out common words

💡 Hint: Think about what helps us to analyze sentences.

Question 2

True or False: Stop-words are always removed from all textual data.

  • True
  • False

💡 Hint: Think about literature and forms of expression.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given the sentence: 'The quick brown fox jumps over the lazy dog.' tokenize it and identify the stop-words.

💡 Hint: Look for common words that don’t add meaning.

Question 2

In what scenarios might lemmatization be more beneficial than stemming? Provide at least two examples.

💡 Hint: Consider tasks where word meaning is crucial.

Challenge and get performance evaluation