MS-DS Master of Data science FREE Master of Data Science Natural Language Processing Questions and Answers 2

Question 1

Which technique represents words as dense, low-dimensional vectors learned from large corpora?

Accepted Answer

Word embeddings

Answer

One-hot encoding

Answer

Bag of words

Answer

TF-IDF weighting

Question 2

What is the primary purpose of the attention mechanism in sequence-to-sequence models?

Accepted Answer

To allow the decoder to focus on relevant parts of the input sequence

Answer

To reduce the vocabulary size

Answer

To speed up training convergence

Answer

To eliminate the need for word embeddings

Question 3

In named entity recognition (NER), which label would typically be assigned to the word 'Stanford' in the sentence 'She studied at Stanford'?

Accepted Answer

Organization

Answer

Person

Answer

Location

Answer

Miscellaneous

Question 4

What problem does beam search address during text generation compared to greedy decoding?

Accepted Answer

It explores multiple candidate sequences to find a higher-probability output

Answer

It guarantees finding the globally optimal sequence

Answer

It reduces memory usage during decoding

Answer

It eliminates the need for a language model

Question 5

Which evaluation metric computes the geometric mean of n-gram precisions between generated and reference text?

Accepted Answer

BLEU

Answer

ROUGE-L

Answer

METEOR

Answer

Perplexity

Question 6

What is the vanishing gradient problem in the context of recurrent neural networks for NLP?

Accepted Answer

Gradients shrink exponentially over long sequences, preventing learning of long-range dependencies

Answer

The model produces identical outputs regardless of input length

Answer

Training loss becomes negative during backpropagation

Answer

Word embeddings converge to zero vectors after many epochs