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# MCQ in Natural Language Processing, Quiz questions with answers in NLP, Top interview questions in NLP with answers, language model quiz questions, MLE in NLP

## Multiple Choice Questions and Answers in NLP Set - 11

1. Which of the following models can be estimated by maximum likelihood estimator?
(a) Support Vector Machines
(b) Maximum Entropy Model
(c) k Nearest Neighbor
(d) Naive Bayes.

 Answer: (b) Maximum Entropy Model and (d) Naïve Bayes In Naïve Bayes, the parameters q(y) and q(x|y) can be estimated from data using maximum likelihood estimation.

2. Suppose a language model assigns the following conditional n-gram probabilities to a 3-word test set: 1/4, 1/2, 1/4. Then P(test-set) = 1/4 * 1/2 * 1/4 = 0.03125. What is the perplexity?
(a) 0.25
(b) 0.03125
(c) 32
(d) 3.175

 Answer: (d) 3.175 Given, |w| = 3, P(test-set) = 0.03125.

3. Assume a corpus with 350 tokens in it. We have 20 word types in that corpus (V = 20). The frequency (unigram count) of word types “short” and “fork” are 25 and 15 respectively. Which of the following is the probability of “short” (PMLE(“short”))?
(a) 25/350
(b) 26/370
(c) 26/350
(d) 25/370

 Answer: (a) 25/350 For the Unigram model, the Maximum Likelihood Estimate (MLE) can be calculated as follows; P(w) = count(w) / count(tokens) = 25/350

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