Multiple Choice Questions (MCQ) in Natural Language Processing (NLP) with answers, NLP Quiz questions with answers
NLP MCQ with answers
1. N-grams are defined as the combination of
N keywords together. How many bi-grams can be generated from the given
sentence: Gandhiji is the father of our nation
a) 7
b) 6
c) 8
d) 9
Answer: (b)
Bigrams are sequence
of two words that are appearing adjacent in a sentence.
In the given sentence,
we have 6 bigrams, ‘Gandhiji is’, ‘is the’, ‘the father’, ‘father of’, ‘of
our’, and ‘our nation’.
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2.
Which of the following techniques can be used for the purpose of keyword
normalization, the process of converting a keyword into its meaningful base
form?
a) Lemmatization
b) Levenshtein distance
c) Morphing
d) Stemming
Answer: (a)
Lemmatization is the
process of mapping an inflected or derived word to its base form (root word).
The base form is the meaningful stem.
Stemming
is the process like lemmatization but need not end up in a meaningful word as
the base form.
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3.
Which of the following areas where NLP can be useful?
a) Automatic text
summarization
b) Automatic question
answering systems
c) Information retrieval
d) All of the above
Answer: (d)
The given options are
some of the natural language processing applications which are common for all
natural languages.
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4.
Which of the following is the recognized statement by the Maximum Matching
algorithm (Greedy - forward pass only) for string thetabledownthere?
a) the table down there
b) theta bled own there
c) both (a) and (b)
d) None of the above
Answer: (b)
Maximum matching
algorithm is a greedy algorithm that requires a dictionary of the language. It
starts by pointing at the beginning of a string. It chooses the longest word
in the dictionary that matches the input at the current position. The pointer
is then advanced past each character in that word. If no word matches, the
pointer is instead advanced one character (creating a one-character word).
The algorithm is then iteratively applied again starting from the new pointer
position.
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5.
You have collected a data of about 10,000 rows of tweet text and no other
information. You want to create a tweet classification model that categorizes
each of the tweets in three buckets – positive, negative and neutral. Which of
the following models can perform tweet classification with regards to context
mentioned above?
a) Naïve Bayes
b) Support Vector Machine
c) Language model
d) None of the above
Answer: (d)
As per the question,
we are given only the data, not the classes of each row of tweet text.
Without the corpus categorized with the categories positive, negative and
neutral, we cannot perform tweet classification.
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