More MCQs with answers on various topics of Natural Languge Processing
MCQ questions and Answers in NLP
1. Which of the following is an advantage of Porter stemmer over a full morphological parser?
a) The stemmer is
better justified from a theoretical point of view
b) The output of a
stemmer is always a valid word
c) The stemmer does
not require a detailed lexicon to implement
d) None of the
above
View Answer
Answer:
(c) The stemmer does not require a detailed
lexicon to implement
The Porter stemming
algorithm is a process for removing suffixes from words in English. The Porter
stemming algorithm was made in the assumption that we don’t have a stem
dictionary (lexicon) and that the purpose of the task is to improve Information
Retrieval performance.
Stemming algorithms are typically
rule-based. You can view them as heuristic process that sort-of lops off the
ends of words.
Stemming
usually refers to a crude heuristic process that chops off the ends of words
in the hope of achieving this goal correctly most of the time, and often
includes the removal of derivational affixes.
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2. Given a set of unigram and bigram probabilities,
what is the probability of the following sequence ‘<s> do Sam I like’ according to the bigram language model?[Refer here: Language modeling in NLP]
P(do|<s>) = 2/11,
P(do|Sam) = 1/11, P(Sam|<s>) = 4/11, P(Sam|do) = 1/8, P(I|Sam) = 4/11, P(Sam|I)
= 2/9, P(I|do) = 2/8, P(I|like) = 2/7, P(like|I) = 3/11, P(do) = 3/8, P(Sam) =
2/11, P(I) = 4/11, P(like) = 5/11
a) 3/11 * 2/11 *
4/11 * 5/11
b) 2/11 * 1/8 * 4/11
* 3/11
c) 2/11 * 1/11 * 2/9
* 2/7
d) 2/11 + 1/11 + 2/9 + 2/7
View Answer
Answer:
(b) 2/11 * 1/8 * 4/11 * 3/11
As per Bigram language model, the probability of the given word sequence can be calculated by
multiplying the bigram conditional probabilities present in the word
sequence.
Bigram
conditional probability = P(current word | previous word)
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3. Which of the following is TRUE about CRF (Conditional Random Field) and HMM (Hidden Markov Model)?
a) CRF is
generative model and HMM is discriminative model
b) Both CRF and HMM
are generative model
c) CRF is discriminative
model and HMM is generative model
d) Both CRF and HMM
are discriminative model
View Answer
Answer:
(c) - CRF is discriminative model and HMM is generative
model
Generative and
discriminative models are probability models.
Generative model
learns the joint probability distribution P(x, y) whereas discriminative
model learns the conditional probability P(y|x).
A
hidden Markov model explicitly describes
the prior distribution on states, not just the conditional distribution of
the output given the current state. It actually gives a joint distribution on
states and outputs. Like any generative model, it means that you could
generate data that follows the same distribution of the input you're
modeling.
Conditional Random Fields
are a type of discriminative classifier, and as such, they model the decision
boundary between the different classes.
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4. Knowledge of the relationship of meaning to the goals and intentions of the speaker is ________
a) Morphology
b) Semantics
c) Pragmatics
d) Discourse
View Answer
Answer:
(c) Pragmatics
Pragmatics focuses on
conversational implicature. Conversational implicature is a process in which
the speaker implies and a listener infers. Simply, it is a study about the
sentences that are not directly spoken. It is the study of how people use
language.
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5. Which of the following NLP problems can be solved with Hidden Markov Model (HMM)?
a) POS tagging
b) Speech
recognition
c) Spelling
correction
d) All of the above
View Answer
Answer:
(d) All of the above
An HMM is a probabilistic
sequence model: given a sequence of units (words, letters, morphemes, sentences,
whatever), it computes a probability distribution over possible sequences of
labels and chooses the best label sequence.
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- Go to Natural Langugage Processing home page
- Go to NLP - MCQ Quiz Home page
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