Natural language processing quiz questions with answers, NLP true false interview questions, NLP quiz questions for competitive exams
NLP TRUE/FALSE Quiz Questions - SET 01
1. HMM for POS tagging problem assumes words are independent from each other.
(a)
TRUE (b)
FALSE
View Answer
Answer:
(b) FALSE
HMM assumes the words are independent from each other given
the tag sequence.
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2. In machine
translation, a parallel corpus is required to estimate the language model.
(a)
TRUE (b)
FALSE
View Answer
Answer:
(b) FALSE
The parallel corpus is used to estimate the translation
probabilities.
|
3. Given a
well-tuned unigram language model p(w|θ)
trained on all the text books in the domain of “Natural Language Processing”,
we can conclude that p(“Natural Language Processing”|θ) > p(“Language
Processing Natural”|θ).
(a)
TRUE (b)
FALSE
View Answer
Answer:
(b) FALSE
In unigram language model we are not considering the word
order. Hence, they should be equal.
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4. Given a unigram language model and a bigram language model estimated on the same text collection without smoothing, perplexity of the unigram language model will be much larger than that of the bigram language model on this same training corpus.
(a)
TRUE (b)
FALSE
View Answer
Answer:
(a) TRUE
Perplexity measures how well a model “fits” the test data. It
uses the probability that the model assigns to the test corpus. It normalizes
for the number of words in the test corpus and takes the inverse.
The probability of individual words is more likely when
compared with probability of bigrams.
The more information the n-gram gives us about the word
sequence, the lower the perplexity (since as Eq. 3.15 showed, perplexity is
related inversely to the likelihood of the test sequence according to the
model).
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5. To make it
computationally feasible, Naive Bayes assumes that features are independent
from each other.
(a)
TRUE (b)
FALSE
View Answer
Answer:
(b) FALSE
Naïve Bayes only assumes conditional independence.
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Related links:
- Go to Natural Langugage Processing home page
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