Practice Modeling (1.3) - Natural Language Processing (NLP) in Depth - Artificial Intelligence Advance

Practice: Modeling

Lesson 1.3Natural Language Processing (NLP) in Depth

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10 questions

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6 sample questions

Question 1.
What does Naive Bayes assume about word independence?
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Think about the 'naive' part of Naive Bayes.
Question 2.
True or False: Support Vector Machines can be used for regression.
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Consider the different types of SVM tasks.
3Question 3.
What is the main assumption of Naive Bayes?
- Words are dependent on each other
- Words are independent of each other
- Naive Bayes cannot classify text
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Recall the definition of 'naive' in Naive Bayes.
4Question 4.
True or False: LSTM can process long sequences effectively.
- True
- False
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Consider how LSTM manages information over time.
Question 5.
Consider a scenario where an email needs to be categorized into spam or not spam. Describe how you would use Naive Bayes for classification, including the relevant features.
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Focus on how word frequency influences classification.
Question 6.
In implementing a chatbot, explain how you might use both SVM and BERT to enhance user experience. Include strengths and weaknesses of each model.
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Think about separating tasks based on strengths.

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