Practice Text Preprocessing - 9.2.1 | 9. Natural Language Processing (NLP) | Data Science Advance
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

games

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is tokenization?

πŸ’‘ Hint: What do we do to make text easier to analyze?

Question 2

Easy

Give an example of a stop-word.

πŸ’‘ 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

πŸ’‘ 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

πŸ’‘ 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.

πŸ’‘ 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.

πŸ’‘ Hint: Consider tasks where word meaning is crucial.

Challenge and get performance evaluation