Natural Language Processing Quiz Questions

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Question 1

Which of the following is a pre-trained language model often used in NLP tasks?

  •  Word2Vec

  • GloVe

  • BERT (Bidirectional Encoder Representations from Transformers)

  • FastText

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Question 2

Which deep learning architecture is widely used in natural language processing and involves attention mechanisms?

  • Recurrent Neural Network (RNN)

  • Long Short-Term Memory (LSTM)

  • Transformer

  • Convolutional Neural Network (CNN)

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Question 3

In Python's NLTK library, what function is used for stop word removal?

  • remove_stopwords()

  • nltk.remove_stopwords()

  • stopwords.remove()

  • nltk.corpus.stopwords.words()

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Question 4

What role do pre-trained language models like GPT-3 play in NLP tasks?

  • They eliminate the need for labeled training data

  • They enhance model interpretability

  • They optimize hyperparameter tuning

  • They leverage knowledge learned from large corpora to improve performance

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Question 5

In Python's spaCy library, what method is used for tokenization?

  • tokenize_text()

  • nlp.tokenize()

  • spacy.tokenize()

  • nlp()

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Question 6

What is the purpose of the fit_transform method in Scikit-learn's CountVectorizer class?

  • Tokenization

  • Feature extraction

  • Model training

  • Text summarization

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Question 7

Explain the concept of attention mechanism in the context of NLP.

  • It measures the importance of different parts of the input sequence for the output

  • It reduces model complexity

  • It handles imbalanced classes in text classification

  • It optimizes the training speed of deep learning models

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Question 8

In the context of neural network models for NLP, what is an epoch?

  •  A type of layer

  • A measure of model interpretability

  • One complete pass through the entire training dataset

  • A unit of word embedding

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Question 9

Which pre-processing step is essential for handling case sensitivity in text analysis?

  • Tokenization

  • Stop word removal

  • Lemmatization

  • Lowercasing

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Question 10

What is the role of an embedding layer in neural network models for NLP?

  • Reducing overfitting
     

  • Extracting features from text

  • Representing words as dense vectors

  • Enhancing interpretability

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