Natural Language Processing MCQs

1. What does NLP stand for? A) Natural Language Processing B) Neural Language Processing C) Numerical Language Processing D) Nonlinear Language Processing Answer: A) Natural Language Processing 2. Which of the following is a common task in NLP? A) Sentiment analysis B) Image classification C) Time series forecasting D) Clustering Answer: A) Sentiment analysis 3. What is “tokenization” in NLP? A) The process of breaking down text into individual words or phrases B) The process of encoding text into numerical format C) The process of translating text into another language D) The process of summarizing text Answer: A) The process of breaking down text into individual words or phrases 4. Which technique is used for converting words into numerical vectors in NLP? A) One-hot encoding B) K-means clustering C) Principal Component Analysis (PCA) D) Convolutional Neural Networks (CNNs) Answer: A) One-hot encoding 5. What is the purpose of “stemming” in NLP? A) To reduce words to their base or root form B) To categorize text into predefined topics C) To translate text into different languages D) To correct grammatical errors Answer: A) To reduce words to their base or root form 6. Which of the following is an example of a “stop word”? A) “the” B) “machine” C) “learning” D) “model” Answer: A) “the” 7. What is “Named Entity Recognition” (NER) used for in NLP? A) To identify and classify entities such as people, organizations, and locations in text B) To generate new text based on the given input C) To translate text into another language D) To summarize large documents Answer: A) To identify and classify entities such as people, organizations, and locations in text 8. Which model is commonly used for generating word embeddings? A) Word2Vec B) K-means clustering C) Support Vector Machines (SVM) D) Random Forest Answer: A) Word2Vec 9. What does the term “n-gram” refer to in NLP? A) A contiguous sequence of n items from a given text B) A type of neural network layer C) A text summarization technique D) A method for translating languages Answer: A) A contiguous sequence of n items from a given text 10. Which technique is used to improve text classification accuracy by combining multiple models? A) Ensemble learning B) Data augmentation C) Dimensionality reduction D) Hyperparameter tuning Answer: A) Ensemble learning 11. What is “part-of-speech tagging” (POS tagging) in NLP? A) Assigning parts of speech (e.g., nouns, verbs) to each word in a text B) Translating text into different languages C) Summarizing text into shorter sentences D) Detecting the sentiment of a text Answer: A) Assigning parts of speech (e.g., nouns, verbs) to each word in a text 12. Which algorithm is often used for text classification tasks in NLP? A) Naive Bayes B) Decision Trees C) K-means clustering D) Principal Component Analysis (PCA) Answer: A) Naive Bayes 13. What is “word sense disambiguation” in NLP? A) The process of determining the correct meaning of a word based on context B) The process of identifying named entities in text C) The process of tokenizing text into individual words D) The process of translating text into another language Answer: A) The process of determining the correct meaning of a word based on context 14. Which of the following is a common evaluation metric for text classification models? A) F1-score B) Mean Squared Error (MSE) C) Accuracy D) Root Mean Squared Error (RMSE) Answer: A) F1-score 15. What does “LSTM” stand for in the context of NLP? A) Long Short-Term Memory B) Linear Short-Term Memory C) Logistic Sequential Temporal Model D) Layered Statistical Text Model Answer: A) Long Short-Term Memory 16. Which technique is commonly used for text summarization? A) Extractive summarization B) Generative adversarial networks (GANs) C) K-means clustering D) Principal Component Analysis (PCA) Answer: A) Extractive summarization 17. What is “TF-IDF” in NLP? A) Term Frequency-Inverse Document Frequency, a statistical measure used to evaluate the importance of a word in a document B) Temporal Frequency-Inverse Document Factor, a method for text classification C) Textual Frequency-Inverse Data Feature, used for summarization D) Term Factor-Inverse Document Frequency, a type of embedding Answer: A) Term Frequency-Inverse Document Frequency, a statistical measure used to evaluate the importance of a word in a document 18. What does “semantic similarity” refer to in NLP? A) The degree to which two pieces of text have similar meanings B) The grammatical structure of sentences C) The number of unique words in a text D) The syntactic structure of sentences Answer: A) The degree to which two pieces of text have similar meanings 19. Which of the following models is used for machine translation tasks? A) Transformer B) K-means clustering C) Decision Trees D) Random Forest Answer: A) Transformer 20. What is “attention mechanism” in NLP models? A) A technique that allows the model to focus on different parts of the input text when making predictions B) A type of data preprocessing C) A method for feature extraction D) A type of regularization Answer: A) A technique that allows the model to focus on different parts of the input text when making predictions 21. What is “contextual embeddings” in NLP? A) Embeddings that capture the meaning of words based on the context in which they appear B) Fixed representations of words regardless of context C) Numerical representations of character sequences D) Pretrained embeddings used for feature extraction Answer: A) Embeddings that capture the meaning of words based on the context in which they appear 22. Which of the following is a popular library for NLP tasks in Python? A) NLTK B) OpenCV C) Scikit-learn D) TensorFlow Answer: A) NLTK 23. What is “Word2Vec”? A) A model for learning word embeddings B) A text classification algorithm C) A data preprocessing method D) A type of neural network architecture Answer: A) A model for learning word embeddings 24. What is “BERT” used for in NLP? A) A model for generating contextual embeddings B) A method for tokenization C) A type of clustering algorithm D) A feature selection technique Answer: A) A model for generating contextual embeddings 25. What does “latent semantic analysis” (LSA) aim to achieve? A) To discover the underlying structure in a collection of texts by analyzing relationships between terms and documents B) To translate text into different languages C) To summarize large documents D) To classify text into predefined categories Answer: A) To discover the underlying structure in a collection of texts by analyzing relationships between terms and documents 26. Which technique helps in dealing with long-term dependencies in text data? A) Long Short-Term Memory (LSTM) B) Principal Component Analysis (PCA) C) Naive Bayes Classifier D) K-means clustering Answer: A) Long Short-Term Memory (LSTM) 27. What is the “bag-of-words” (BoW) model used for? A) Representing text data as a collection of words without considering the order B) Generating word embeddings C) Extracting features from text D) Tokenizing text into phrases Answer: A) Representing text data as a collection of words without considering the order 28. Which algorithm is commonly used for topic modeling in NLP? A) Latent Dirichlet Allocation (LDA) B) K-means clustering C) Support Vector Machines (SVM) D) Random Forest Answer: A) Latent Dirichlet Allocation (LDA) 29. What does “preprocessing” in NLP typically include? A) Cleaning and transforming text data before analysis B) Training a model on the data C) Evaluating model performance D) Generating new data Answer: A) Cleaning and transforming text data before analysis 30. What is “text generation” in NLP? A) Creating new text based on a given input or model B) Classifying text into predefined categories C) Extracting named entities from text D) Summarizing large documents Answer: A) Creating new text based on a given input or model

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