DSE Natural Language Processing Fundamentals 4

Question 1

Which problem does the 'vanishing gradient' issue primarily affect in recurrent neural networks for NLP?

Accepted Answer

Learning long-range dependencies in sequences

Answer

Gradients shrink exponentially as they propagate back through many time steps, making it hard for RNNs to capture long-range dependencies.

Question 2

In coreference resolution, what is the task the model must solve?

Accepted Answer

Clustering mentions in text that refer to the same real-world entity

Answer

Coreference resolution groups all mentions (nouns, pronouns, noun phrases) that refer to the same entity into clusters.

Question 3

What is the primary goal of sequence-to-sequence (seq2seq) models in NLP?

Accepted Answer

Map an input sequence of tokens to an output sequence of potentially different length

Answer

Seq2seq models use an encoder to compress the input and a decoder to generate an output sequence, used in tasks like translation and summarization.

Question 4

Which of the following is an example of an extractive summarization approach?

Accepted Answer

Selecting and concatenating important sentences directly from the source

Answer

Extractive summarization selects actual sentences or phrases from the original document rather than generating new text.

Question 5

What does the term 'polysemy' mean in linguistics and NLP?

Accepted Answer

A single word form that carries multiple related meanings

Answer

Polysemy refers to a single word having multiple related senses, such as 'bank' meaning a financial institution or a riverbank.

DSE - Data Science Practice Test