NLP_B1_19.09

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

• 6

• 7

• 9

• 8

In the sentence, “They bought a blue house”, the underlined part is an example of _____.

• Noun phrase

• Verb phrase

• Adverbial phrase

• Prepositional phrase

4-grams are better than trigrams for part-of-speech tagging

• False

• True

What will be the perplexity value if you calculate the perplexity of an unsmoothed language model on a test corpus with unseen words

• Infinity

• None of the above

• any non-zero value

• 0

Which of the following NLP tasks use sequential labeling technique

• Speech recognition

• All of the above

• Named Entity Recognition

• POS tagging

What is the number of trigrams in a normalized sentence of length n words

• n

• n-1

• n-2

• n-3

Which of the following techniques is commonly used for smoothing in n-gram models?

• K-means clustering

• Latent Dirichlet Allocation (LDA)

• Principal Component Analysis (PCA)

• Add-one (Laplace) smoothing

What is the primary purpose of an n-gram model in natural language processing?

• To classify text into predefined categories

• To generate random sequences of text

• To estimate the probability of a word sequence

• To identify named entities in text

In a bigram model, which probability is used to estimate the likelihood of the word sequence "w1 w2 w3"?

• 
  P(w1)⋅P(w2∣w1)⋅P(w3∣w2)

• 
  P(w1∣w2)⋅P(w2∣w3)⋅P(w3)

• 
  P(w1∣w2,w3)⋅P(w2∣w1)⋅P(w3)

• P(w2∣w1)⋅P(w3∣w2)

How does the smoothing technique help in n-gram models?

• By reducing the computational complexity

• By increasing the number of n-grams in the model

• By handling zero probabilities for unseen n-grams

• By increasing the corpus size