Advanced Database Management System - Tutorials and Notes: Natural Language Processing MCQ 11

Tuesday, 26 May 2020

Natural Language Processing MCQ 11

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.

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.
perplexity calculation

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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