Practice Tokenization (27.3.1) - Concepts of Natural Language Processing (NLP)

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6 sample questions

Question 1.
What is tokenization?
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Think about how language can be divided into parts.
Question 2.
Give an example of a token.
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Remember, it can be one word or a group of words.
3Question 3.
What is the main purpose of tokenization in NLP?
- To break text into sentences
- To process natural language efficiently
- To generate text
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Focus on what enables better machine understanding.
4Question 4.
Tokenization is only important for word-level analysis. True or False?
- True
- False
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Remember its applications in both forms of text processing.
Question 5.
Explain how context can impact tokenization effects. Provide an example.
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Think about sentences where a word might have more than one interpretation.
Question 6.
Design a simple tokenization function that differentiates between words and punctuation.
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Consider how regular expressions can help parse text effectively.

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