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