Practice Word Embeddings And Representations (2) - Natural Language Processing (NLP) in Depth

Practice: Word Embeddings and Representations

Lesson 2Natural Language Processing (NLP) in Depth

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10 questions

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6 sample questions

Question 1.
What is the primary purpose of word embeddings?
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Consider how computers handle language.
Question 2.
How does the Skip-gram model function in word2vec?
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Think about what the model uses as input.
3Question 3.
What does word2vec primarily do?
- Generates images
- Finds word meanings
- Creates word embeddings
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Think about word representation.
4Question 4.
True or False: GloVe uses local word contexts to create word vectors.
- True
- False
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Consider the difference between local and global statistics.
Question 5.
A company wants to implement sentiment analysis using an NLP model. Discuss whether they should use static or contextual embeddings and justify your choice.
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Think about how emotions can change in different contexts.
Question 6.
Imagine you need to build a chatbot that understands varied phrasing related to banking services. Discuss which embedding technique would be most suitable and why.
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Consider how phrases can be interpreted in multiple ways.

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