Natural Language Processing Nptel Week 1 Quiz Answers 2026

Last updated: January 19, 2026

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Natural Language Processing Nptel Week 1 Quiz Answers (Jan-Apr 2026)

Que.1
If Zipf’s Law holds and the most frequent word (r = 1) appears 10,000 times, how many times should the word at rank 50 appear?

a) 500
b) 200
c) 100
d) 50

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Que.2
Consider a system employing the standard Porter Stemming algorithm for text normalization. If the token “computational” is processed, what is the final stemmed output?

a) compute
b) comput
c) computa
d) computate

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Que.3
Consider the sentence:
“Rose rose to put rose roes on her rows of roses.”
Ignoring case and punctuation (i.e., after normalization), what are the Word Token (N) and Word Type (|V|) counts?

a) Tokens: 11, Types: 9
b) Tokens: 11, Types: 8
c) Tokens: 10, Types: 8
d) Tokens: 11, Types: 10

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Que.4
Using Heaps’ Law (k = 50, β = 0.5), how does the estimated vocabulary size change if the corpus size (N) increases from 1 million tokens to 4 million tokens?

a) It doubles
b) It quadruples
c) It increases by a factor of 50
d) It remains roughly the same

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Que.5
Calculate the TTR (Type-token Ratio) for the sentence:
“the cat sat on the mat”
(Treat the sentence as lower-case and tokenized by space.)

a) 0.69
b) 0.83
c) 1.20
d) 1.00

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Que.6
Assuming a corpus follows Heaps’ Law |V| = kN^β, derive the relationship describing how TTR changes as a function of corpus size N.

a) TTR(N) = kN^β
b) TTR(N) = kN^(β−1)
c) TTR(N) = k log(N)
d) TTR(N) = N^β / k

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Que.7
Given that the 10th most frequent word in a corpus (which closely follows Zipf’s Law) has a probability of occurrence of 0.012, what is the frequency of the most frequent word if the total size of the corpus is 10,000 words?

a) 120
b) 1,000
c) 1,200
d) 12,000

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Que.8
Two words, w₁ and w₂, have ranks r₁ = 100 and r₂ = 10,000 respectively. According to the empirical correlation between rank and number of meanings (m), what is the expected ratio of their meanings m₁ : m₂?

a) 10 : 1
b) 100 : 1
c) 1 : 10
d) 1 : 100

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Que.9
Identify the category of affix represented by re- in the context of the word reboot.

a) Suffix
b) Prefix
c) Stem
d) Infix

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Que.10
Heaps’ Law models the growth of vocabulary size |V| as a function of the collection size N, given by |V| = kN^β. In a typical English corpus, the parameter β usually falls in the range 0.4–0.6. What does the condition β < 1 imply about the nature of language scaling?

a) The vocabulary size grows exponentially relative to the corpus size
b) The rate of discovering new words increases as the corpus gets larger
c) There are diminishing returns; fewer new words are discovered as more text is processed
d) The vocabulary size is fixed and does not change after a certain threshold T

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(Jan-Apr 2025)

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1. In a corpus, you found that the word with rank 4th has a frequency of 250. What can be the best guess for the rank of a word with frequency 125?

1. 2
2. 4
3. 6
4. 8

Answer :- b

2. In the sentence, “In Delhi I took my hat off. But I can’t put it back on.”, total number of
word tokens and word types are:

1. 14, 13
2. 13, 14
3. 15, 14
4. 14, 15

Answer :- a

3. Let the rank of two words, w1 and w2, in a corpus be 1600 and 100, respectively. Let m1 and m2 represent the number of meanings of w1 and w2 respectively. The ratio m1 : m2 would tentatively be

1. 1:4
2. 4:1
3. 1:2
4. 2:1

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4. What is the valid range of type-token ratio of any text corpus?

TTRE(0,1] (excluding zero)
TTRe[0,1]
TTRE[-1,1]
TTRE[0, +∞] (any non-negative number)

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5. If first corpus has TTR₁ = 0.06 and second corpus has TTR₂ = 0.105, where TTR₁ and TTR₂ epresents type/token ratio in first and second corpus respectively, then

1. First corpus has more tendency to use different words.
2. Second corpus has more tendency to use different words.
3. Both a and b
4. None of these

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6. Which of the following is/are true for the English Language?

1. Lemmatization works only on inflectional morphemes and Stemming works only on derivational morphemes.
2. The outputs of lemmatization and stemming for the same word might differ.
3. Output of lemmatization are always real words
4. Output of stemming are always real words

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7. An advantage of Porter stemmer over a full morphological parser?