NLP advanced Quiz 8 > Data AI Revolution

NLP advanced Quiz 8

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and respond to text or speech in ways that are both meaningful and useful. NLP combines computational linguistics with machine learning and deep learning to process and analyze vast amounts of natural language data.

1 / 10

What is the output of the Softmax function?

Probability distribution over classes

Word embeddings

Attention weights

Loss values

2 / 10

Which technique uses "attention scores" for token alignment?

LSTM

RNN

Attention Mechanism

BoW

3 / 10

What does the acronym TF-IDF stand for?

Term Frequency-Inverse Document Frequency

Text Frequency-Inverse Data Frequency

Token Frequency-Indexed Document Frequency

Term Frequency-Inverse Data Frequency

4 / 10

What is the difference between stemming and lemmatization?

Lemmatization is faster than stemming

Stemming is rule-based; lemmatization uses vocabulary

Lemmatization removes stop words; stemming does not

Stemming provides grammatically correct words

5 / 10

Which of the following is a pre-trained language model?

SVM

ResNet

BERT

PCA

6 / 10

In BERT, what is the purpose of the "masked language modeling" task?

To predict the next word

To pre-train the model for text classification

To predict masked tokens in a sentence

To generate coherent text sequences

7 / 10

Which transformer architecture underlies GPT models?

Encoder-Decoder

Encoder-only

Decoder-only

RNN-based

8 / 10

What does the Attention Mechanism in NLP primarily address?

Capturing word embeddings

Handling variable-length inputs

Assigning importance to different parts of input

Simplifying training of recurrent neural networks

9 / 10

Which of the following is a disadvantage of using Bag of Words (BoW) for text representation?

It captures semantic meaning effectively

It is computationally efficient

It considers word order

It results in sparse matrices

10 / 10

What is the primary goal of Word2Vec?

To generate text summaries

To create dense vector representations of words

To predict the next word in a sequence

To classify documents

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NLP advanced Quiz 8

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