Practice Vectorization (1.2) - Natural Language Processing (NLP) in Depth

Practice: Vectorization

Lesson 1.2Natural Language Processing (NLP) in Depth

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11 questions

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6 sample questions

Question 1.
What does vectorization do in NLP?
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Think about how computers understand language.
Question 2.
Name one method of vectorization.
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It's a way to assess word importance.
3Question 3.
What is the purpose of vectorization in NLP?
- To convert numbers into text
- To analyze language
- To convert text into numerical vectors
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Think about how computers process data.
4Question 4.
TF-IDF emphasizes common words in documents.
- True
- False
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Consider the purpose of TF-IDF.
Question 5.
Consider a document set containing a lot of reviews for different products. How might you use TF-IDF to prioritize key features highlighted in the reviews?
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Focus on words that appear frequently in certain documents but are rare across the entire set.
Question 6.
Design a small experiment comparing the effectiveness of Word2Vec and GloVe embeddings in a sentiment analysis task. What factors would you consider?
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Think about how word meanings could affect sentiment classification.

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