MCQ - QA2 - AI

What is the main limitation of the bag-of-words model in natural language processing?

• Inability to handle unknown words

• Ignoring word order and context

• Lack of character-level analysis

Which of the following applications is NOT suitable for the bag-of-words model?

• Text classification

• Spam filtering

• Context analysis

What is the primary purpose of stemming in natural language processing?

• To convert words to their meaningful base form

• To ignore word order in sentences

• To improve language identification accuracy

In the phrase "I don't like ice cream," what would be an example of a 1-skip bigram?

• "don't like"

• "like ice"

• "cream I"

What does Laplace (Add-One) smoothing add to every possible N-gram during training?

• A count of 1

• A count of 0

What is the primary advantage of character-level models in language identification tasks?

• Handling unknown words

• Capturing word patterns

Which method considers the context and converts a word to its meaningful base form?

• Stemming

• Lemmatization

What is the purpose of Part-of-Speech (POS) tagging models like Hidden Markov Model (HMM)?

• Categorizing words into their grammatical roles

• Capturing word patterns

What problem do smoothing techniques in N-gram models aim to mitigate?

• Zero probabilities for unseen N-grams

• Lack of understanding of word patterns

• Overly confident predictions

How do word embeddings represent similar words in vector space?

• By placing them in close proximity

• By using the same vector for all words