Are you looking for accurate and updated answers for Week 1 of Natural Language Processing NPTEL assignment? You’re in the right place! Below you’ll find expert-verified answers along with concise explanations to help you complete your assignment quickly and confidently.
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?
Options:
- 2
- 4
- 6
- 8
Answer:✅ 3 (Option c)
Explanation:
According to Zipf’s Law, rank and frequency have an inverse relationship. If frequency halves (250 → 125), the rank approximately doubles (4 → 8). Hence, rank 6 is a close estimate.
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:
Options:
- 14, 13
- 13, 14
- 15, 14
- 14, 15
Answer:✅ 1 (Option a)
Explanation:
- Word Tokens: Total number of words = 14
- Word Types: Unique words = 13
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:
Options:
- 1:4
- 4:1
- 1:2
- 2:1
Answer:✅ 1 (Option a)
Explanation:
Higher rank → lower frequency → fewer meanings. So, a low-ranked word likely has fewer meanings compared to a high-ranked one.
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4. What is the valid range of type-token ratio (TTR) of any text corpus?
Options:
- (0,1]
- [0,1]
- [-1,1]
- [0, +∞]
Answer:✅ (0,1] (Option a)
Explanation:
TTR = Types / Tokens. It’s always a positive fraction ≤ 1.
5. If first corpus has TTR1 = 0.06 and second corpus has TTR2 = 0.105, where TTR1 and TTR2 represent type/token ratios, then:
Options:
- First corpus has more tendency to use different words.
- Second corpus has more tendency to use different words.
- Both
- None
Answer:✅ 2 (Option b)
Explanation:
Higher TTR → more vocabulary diversity. Hence, second corpus has more varied vocabulary.
6. Which of the following is/are true for the English Language?
Options:
- Lemmatization works only on inflectional morphemes and stemming on derivational.
- Outputs of lemmatization and stemming may differ.
- Lemmatization always returns real words.
- Stemming always returns real words.
Answer:✅ b, c
Explanation:
- Lemmatization is context-aware and produces real words.
- Stemming may produce non-dictionary words.
7. An advantage of Porter stemmer over a full morphological parser?
Options:
- Better theoretical basis
- Always produces real words
- Doesn’t require a detailed lexicon
- None
Answer:✅ 3 (Option c)
Explanation:
Porter Stemmer uses simple rules and does not need a full lexicon, unlike parsers.
8. Which of the following are not instances of stemming (as per Porter Stemmer)?
Options:
- are → be
- plays → play
- saw → s
- university → univers
Answer:✅ a, c
Explanation:
Porter stemmer does not reduce “are” to “be” or “saw” to “s”.
9. What is natural language processing good for?
Options:
- Summarizing text
- Generating keywords
- Entity recognition
- All of the above
Answer:✅ 4 (Option d)
Explanation:
NLP is used in summarization, keyword extraction, NER, sentiment analysis, and more.
10. What is the size of unique words in a document where total words = 12,000, K = 3.71, Beta = 0.69?
Options:
- 2421
- 3367
- 5123
- 1529
Answer:✅ 1 (Option a)
Explanation:
This follows Heaps’ Law: V = K * N^β
→ V = 3.71 * (12000)^0.69 ≈ 2421
✅Conclusion
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