1. In a corpus, you found that the word with rank 4th has a frequency of 600. What can be the best guess for the rank of a word with frequency 300?

• 2
• 4
• 8
• 6

Answer:- 8

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

• 14, 13
• 13, 14
• 15, 14
• 14, 15

Answer:- 14, 13

3. Let the rank of two words, w1 and w2, in a corpus be 1600 and 400, respectively. Let m1 and m2 represent the number of meanings of w1 and w2 respectively. The ratio m1 : m2 would tentatively be

• 1:4
• 4:1
• 1:2
• 2:1

Answer:-  1:2

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)

Answer:- TTRe(0, 1] (excluding zero) 

5. If first corpus has TTR, = 0.025 and second corpus has TTR2 = 0.25, where TTR, and TTR2 represents type/token ratio in first and second corpus respectively, then

• First corpus has more tendency to use different words.
• Second corpus has more tendency to use different words.
• Both a and b
• None of these

Answer:- Second corpus has more tendency to use different words.

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

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

Answer:- b. The outputs of lemmatization and stemming for the same word might differ.

c. Output of lemmatization are always real words.

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

• The stemmer is better justified from a theoretical point of view
• The output of a stemmer is always a valid word
• The stemmer does not require a detailed lexicon to implement
• None of the above

Answer:- The stemmer does not require a detailed lexicon to implement.

8. Which of the following are instances of stemming? (as per Porter Stemmer)

• are -> be
• plays -> play
• saw -> s
• university -> univers

Answer:- b. plays -> play 

d.university -> univers

9. What is natural language processing good for?

• Summarize blocks of text
• Automatically generate keywords
• Identifying the type of entity extracted
• All of the above

Answer:- All of the above

10. What is the size of unique words in a document where total number of words = 12000. K = 3.71 Beta = 0.69?

• 2421
• 3367
• 5123
• 1529

Answer:- 2421