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Thursday, 16 April 2020

MCQ with answer in NLP

Multiple Choice Questions (MCQ) in Natural Language Processing (NLP) with answers

MCQ Quiz Questions with Answers in NLP

1. How many trigrams phrases can be generated from the following sentence, after performing stop word removal? Google is one of the most widely used search engine in India.
a) 7  
b) 6  
c) 2   
d) 9
Answer: (c)
Stop words are words which are filtered out before or after processing of natural language data. Any group of words can be chosen as stop words for a given purpose. [Wikipedia]
By most of the stop words list, the words ‘is’, ‘one’, ‘of’, ‘the’, ‘most’, ‘widely’, ‘used’ and ‘in’ are considered as stop words. Hence, we have only two trigrams from the given sentence – ‘Google search engine’ and ‘search engine India’.

2. One of the major challenges that causes almost all stages of Natural Language Processing a hard one is about handling,
a) Ambiguity of sentences 
b) Tokenization 
c) POS tagging  
d) All of the above
Answer: (a)
Ambiguity in computational linguistics is a situation where a word or a sentence may have more than one meaning. That is, a sentence may be interpreted in more than one way. This leads to uncertainty in choosing the right meaning of a sentence especially while processing natural languages by computer.

3. Morphemes that cannot stand alone and are typically attached to another to become a meaningful word is called,
a) Free morphemes     
b) Bound morphemes         
c) Derived morphemes
d) Lexical morphemes
Answer: (b)

Morpheme is the smallest meaningful parts of words. Stems (base form of words) and affixes are called as free and bound morphemes respectively.

Stem is free morpheme because it provides the main meaning of the word.
Affix is bound morpheme because it is used to provide additional meanings to a stem.
Eg. ‘cat’ + ’-s’ = ‘cats’. Here, ‘cat’ is the free morpheme and ‘-s’ is the bound morpheme.   

4. In an HMM, tag transition probabilities measure
a) The likelihood of a POS tag given a word
b) The likelihood of a POS tag given the preceding tag
c) The likelihood of a word given a POS tag
d) The likelihood of a POS tag given all preceding tags
Answer: (b)
In POS tagging using HMM, POS tags represent the hidden states. The tag transition probabilities refer to state transition probabilities in HMM.
Tag transition probability = P(ti|ti-1) = C(ti-1 ti)/C(ti-1) = the likelihood of a POS tag ti given the previous tag ti-1.

5. Morphotactics is a model of
a) Spelling modifications that may occur during affixation
b) How and which morphemes can be affixed to a stem
c) All affixes in the English language
d) Ngrams of affixes and stems


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