# 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

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__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.

**View Answer**Answer:
(b) Maximum Entropy Model and (d) Naïve BayesIn 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

**View Answer**
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” (P

_{MLE}(“short”))?
(a) 25/350

(b) 26/370

(c) 26/350

(d) 25/370

**View Answer**Answer:
(a) 25/350For 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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