Virtual Labs

What is an N-Gram in natural language processing?

a: A sequence of N words or tokens Explanation

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b: A grammar rule Explanation

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c: A type of neural network Explanation

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d: A punctuation mark Explanation

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Which of the following is a bigram?

a: natural language Explanation

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b: language Explanation

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c: natural Explanation

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d: processing natural language Explanation

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Why do N-Gram models use the Markov assumption?

a: To simplify probability calculations by considering only a limited context Explanation

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b: To increase the complexity of the model Explanation

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c: To ignore word order Explanation

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d: To model all possible word dependencies Explanation

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Which of the following is a common application of N-Gram models?

a: Language modeling for speech recognition Explanation

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b: Image classification Explanation

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c: Sorting numbers Explanation

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d: Database indexing Explanation

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What is the main advantage of using trigrams over bigrams?

a: Trigrams consider a longer context, potentially improving prediction accuracy Explanation

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b: Trigrams require less data than bigrams Explanation

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c: Trigrams ignore word order Explanation

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d: Trigrams are not used in NLP Explanation

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What is a major limitation of N-Gram models as N increases?

a: Data sparsity and increased computational requirements Explanation

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b: They become more accurate Explanation

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c: They ignore word order Explanation

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d: They require less training data Explanation

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A bigram model assigns P(A|B) = 0.5 and P(B|START) = 0.4. What is the probability of the sequence START B A?

a: 0.2 Explanation

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b: 0.9 Explanation

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c: 0.5 Explanation

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d: 0.4 Explanation

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What does smoothing accomplish in N-Gram models?

a: Assigns nonzero probabilities to unseen N-Grams Explanation

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b: Removes all rare words from the corpus Explanation

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c: Increases the N value arbitrarily Explanation

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d: Ignores word order Explanation

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Which of the following is NOT a use case for N-Gram models?

a: Predicting the next word in a sentence Explanation

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b: Speech recognition Explanation

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c: Image segmentation Explanation

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d: Spelling correction Explanation

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Which of the following best describes the chain rule in the context of N-Gram models?

a: It decomposes the probability of a sequence into conditional probabilities Explanation

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b: It ignores the order of words in a sequence Explanation

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c: It increases the N value arbitrarily Explanation

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d: It is used only for unigrams Explanation

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