NLP Critical Thinking Quiz Form
Test your knowledge and reasoning skills in Natural Language Processing. Answer all questions below.
1. Which of the following is an example of tokenization in NLP?
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Breaking text into words
Assigning part-of-speech tags
Translating text to another language
Generating a summary
2. Define 'embedding' as used in NLP.
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3. What is the main purpose of the attention mechanism in transformer models?
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To focus on relevant parts of the input sequence
To reduce model size
To increase training speed
To generate random outputs
4. Provide a short example of a named entity in the sentence: "Apple released a new iPhone in California."
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5. Which evaluation metric is commonly used for classification tasks in NLP?
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F1 Score
BLEU Score
Edit Distance
ROUGE Score
6. In one sentence, explain the difference between stemming and lemmatization.
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7. What is the output dimension of a softmax function applied to a vector of length 5?
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8. Match the NLP task to its best description.
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Rows
Description
Text Classification
Classifying text into categories
Identifying entities in text
Generating new text
Translating text to another language
Named Entity Recognition
Classifying text into categories
Identifying entities in text
Generating new text
Translating text to another language
Text Generation
Classifying text into categories
Identifying entities in text
Generating new text
Translating text to another language
Machine Translation
Classifying text into categories
Identifying entities in text
Generating new text
Translating text to another language
9. Which algorithm is most associated with word vector training?
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Word2Vec
Decision Tree
Linear Regression
Naive Bayes
10. In your own words, explain why context is important in NLP.
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