Multiple Choice Questions (MCQ) in Natural Language Processing (NLP) with answers
NLP MCQ with answers
1.
In a HMM, the possible state transitions are from state JJ to states NN, VB, JJ
and RB. Following are the known state transitions probabilities;
P(NNJJ)
= 1/4. P(VBJJ) = 1/6, and P(JJJJ) = 1/3.
What
is the transition probability value of P(RBJJ)?
a) 1/4
b) 1/2
c) 1/5
d) 1/3
Answer: (a)
The sum of all outgoing links from any state to other states should
be 1.
In
our question, the possible state transitions from JJ are 4.

2.
Suppose we want to calculate a probability for the sequence of observations
{‘Dry’,’Rain’}. If the following are the possible hidden state sequences, then P(‘Dry’
‘Rain’) = .
{‘Low’,
‘Low’}, {‘Low’, ‘High’}, {‘High’, ‘Low’}, and {‘High’, ‘High’}
a) P(‘Dry’ ‘Rain’, ‘Low’ ‘Low’) * P(‘Dry’ ‘Rain’, ‘Low’ ‘High’) * P(‘Dry’
‘Rain’, ‘High’ ‘Low’) * P(‘Dry’ ‘Rain’, ‘HIgh’ ‘High’)
b) P(‘Dry’ ‘Rain’, ‘Low’
‘Low’) + P(‘Dry’ ‘Rain’, ‘Low’ ‘High’) + P(‘Dry’ ‘Rain’, ‘High’ ‘Low’) +
P(‘Dry’ ‘Rain’, ‘HIgh’ ‘High’)
c) P(‘Dry’ ‘Rain’) * P(
‘Low’ ‘High’) * P( ‘High’ ‘Low’) * P(‘HIgh’ ‘High’)
d) P(‘Dry’ ‘Rain’) + P(
‘Low’ ‘High’) + P( ‘High’ ‘Low’) + P(‘HIgh’ ‘High’)
Answer: (b)
We are given the observation sequence {‘Dry’, ‘Rain’}. For
computing the probability of observation sequence given no particular hidden state sequence, then we need to compute the joint probability of observation
and all possible hidden state sequences and sum all these conditional
probabilities to calculate P(‘Dry’ ‘Rain).

3.
[This
question is in continuation with the previous one.] Which
of the following best describes the probability of observation sequence {‘Dry’,’Rain’}
given a hidden state ‘Low’ for the observation ‘Dry’?
a) P(‘Dry’ ‘Rain’, ‘Low’
‘Low’) + P(‘Dry’ ‘Rain’, ‘Low’ ‘High’) + P(‘Dry’ ‘Rain’, ‘High’ ‘Low’) +
P(‘Dry’ ‘Rain’, ‘HIgh’ ‘High’)
b) P(‘Dry’ ‘Rain’, ‘Low’ ‘Low’) * P(‘Dry’ ‘Rain’, ‘Low’ ‘High’) *
P(‘Dry’ ‘Rain’, ‘High’ ‘Low’) * P(‘Dry’ ‘Rain’, ‘HIgh’ ‘High’)
c) P(‘Dry’ ‘Rain’, ‘Low’ ‘Low’)
* P(‘Dry’ ‘Rain’, ‘Low’ ‘High’)
d) P(‘Dry’ ‘Rain’, ‘Low’
‘Low’) + P(‘Dry’ ‘Rain’, ‘Low’ ‘High’)
Answer: (d)
If the observation sequence given without any particular state
sequence, we need to compute the joint probabilities of the observation for
all possible hidden state sequences and sum all these conditional
probabilities.
In
this question, one of the hidden states was given as ‘Low’. Hence, there are
only two possibilities ‘Low’ or ‘High’ for the observation ‘Rain’. Hence the
answer is (d).

4.
_________ is the type of morphology that changes the word category and affects
the meaning.
a) Inflectional
b) Derivational
c) Cliticization
d) All of the above
Answer: (b)
Derivation creates
different words from the same lemma. The new words formed through
derivational morphology may be a stem for another affix and usually have
different meaning than the stem.
On
the other hand, inflectional morphology usually does not change the POS
category or the word meaning.

5.
computer
vs computational is an example of ______ morphology.
a) Inflectional
b) Derivational
c) Cliticization
d) None of the above
Answer: (b)
It is derivational
morphology because both are different words under different categories
(computer – noun, computational – adjective) and with different meanings.

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