Natural Language Processing (NLP): Key Techniques and Algorithms MCQ Exam
Questions: 30
Questions
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1. Which of the following is a major task in Natural Language Processing (NLP)?
- a) Text classification
- b) Sentiment analysis
- c) Named entity recognition
- d) All of the above
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2. What is the purpose of tokenization in NLP?
- a) To split text into individual words or phrases
- b) To identify the language of the text
- c) To assign labels to words
- d) To remove stop words from the text
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3. Which of the following is a common technique used to represent words in a continuous vector space in NLP?
- a) One-hot encoding
- b) Word2Vec
- c) TF-IDF
- d) LSTM
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4. What does the term "stemming" refer to in NLP?
- a) Extracting synonyms from a word
- b) Reducing words to their root forms
- c) Removing punctuation from text
- d) Identifying named entities in text
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5. Which algorithm is commonly used for part-of-speech tagging in NLP?
- a) Naive Bayes
- b) Hidden Markov Model
- c) K-means clustering
- d) Support Vector Machine
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6. Which of the following is NOT an example of a language model used in NLP?
- a) N-gram model
- b) Transformer model
- c) Word2Vec
- d) Random forest model
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7. What is the key advantage of using a Transformer model in NLP?
- a) It can process text sequentially
- b) It can process long-range dependencies efficiently
- c) It works faster than traditional RNN models
- d) It uses a small number of layers
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8. Which of the following is a method used to reduce the dimensionality of word representations in NLP?
- a) Word2Vec
- b) Latent Semantic Analysis (LSA)
- c) Long Short-Term Memory (LSTM)
- d) Decision trees
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9. What is the function of the "attention mechanism" in a Transformer model?
- a) It focuses on specific parts of the input sequence while generating output
- b) It classifies the input sequence into predefined categories
- c) It filters out noisy data from the input
- d) It increases the size of the model
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10. What is a key characteristic of a Recurrent Neural Network (RNN) in NLP?
- a) It processes input data in parallel
- b) It uses a loop to process sequences of data
- c) It is primarily used for image processing tasks
- d) It works with fixed-size input data
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11. Which of the following techniques is commonly used for measuring the similarity between two pieces of text in NLP?
- a) Cosine similarity
- b) Jaccard similarity
- c) Euclidean distance
- d) All of the above
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12. Which of the following is a commonly used NLP technique for sentiment analysis?
- a) Logistic regression
- b) Latent Dirichlet Allocation (LDA)
- c) Naive Bayes classifier
- d) K-means clustering
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13. What does the term "word embeddings" refer to in NLP?
- a) Mapping words into a high-dimensional vector space
- b) A method to split text into individual words
- c) Removing punctuation from text
- d) A method for tokenizing text
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14. Which of the following models is based on the idea of "self-attention" in NLP?
- a) LSTM
- b) Transformer
- c) CNN
- d) Naive Bayes
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15. What does the "bag-of-words" model represent in NLP?
- a) A method for assigning weights to words based on their importance
- b) A technique to convert text into numerical form by counting word occurrences
- c) A method for splitting sentences into individual characters
- d) A model for representing the meaning of a sentence as a single vector
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16. What is the purpose of using "TF-IDF" (Term Frequency-Inverse Document Frequency) in NLP?
- a) To convert words into one-hot vectors
- b) To find the most frequent words in a corpus
- c) To evaluate the importance of a word in a document relative to a corpus
- d) To create embeddings for words
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17. Which of the following is a key challenge in NLP?
- a) Identifying the meaning of homonyms
- b) Handling large-scale image datasets
- c) Training models with small amounts of data
- d) Reducing computational resources
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18. What is the purpose of using the "GloVe" (Global Vectors for Word Representation) model in NLP?
- a) To calculate word frequency
- b) To represent words as vectors in a continuous vector space
- c) To remove stop words from the text
- d) To split text into characters
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19. Which of the following is a technique used to handle out-of-vocabulary (OOV) words in NLP?
- a) Using pre-trained word embeddings
- b) Tokenization
- c) Cross-validation
- d) Weight regularization
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20. What is the purpose of "dependency parsing" in NLP?
- a) To identify the grammatical structure of a sentence and the relationships between words
- b) To convert text into word embeddings
- c) To split sentences into individual words
- d) To classify text into predefined categories
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21. What is the main advantage of using a "pre-trained language model" like BERT in NLP tasks?
- a) It allows for faster training on small datasets
- b) It automatically processes sequences in parallel
- c) It improves performance on a variety of NLP tasks without task-specific training
- d) It requires less computational power
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22. Which of the following is used to assess the relevance of a word in a document or corpus in NLP?
- a) TF-IDF
- b) Word2Vec
- c) K-means clustering
- d) Latent Dirichlet Allocation (LDA)
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23. In NLP, what is the purpose of lemmatization?
- a) To remove stop words
- b) To reduce words to their dictionary form
- c) To convert all words to lowercase
- d) To split words into individual characters
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24. In NLP, what is "named entity recognition" (NER) used for?
- a) Identifying named entities such as people, locations or organizations in text
- b) Classifying text into predefined categories
- c) Extracting sentiment from a piece of text
- d) Segmenting text into words
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25. What is the role of "bigram" and "trigram" models in NLP?
- a) To capture the relationship between words in consecutive pairs (bigrams) or triplets (trigrams)
- b) To classify text into predefined categories
- c) To map words to fixed-length vectors
- d) To extract sentiment from text
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26. Which algorithm is commonly used for text classification in NLP?
- a) Decision trees
- b) K-means clustering
- c) Support Vector Machine (SVM)
- d) Naive Bayes
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27. Which of the following is a key challenge in machine translation in NLP?
- a) Handling word ambiguities and context-dependent meanings
- b) Reducing the dimensionality of word embeddings
- c) Training models with a large vocabulary
- d) Identifying sentence structure
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28. What is a "collocation" in the context of NLP?
- a) A statistical measure of the importance of a word in a document
- b) A sequence of words that frequently occur together in a language
- c) A technique for reducing word vectors to a lower dimensionality
- d) A process of creating sentence-level embeddings
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29. What does "language modeling" in NLP typically involve?
- a) Predicting the next word in a sequence of words based on context
- b) Reducing words to their root form
- c) Removing stop words from text
- d) Identifying named entities in a document
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30. Which of the following techniques can be used for text generation in NLP?
- a) Sequence-to-sequence models
- b) Decision trees
- c) K-means clustering
- d) Principal Component Analysis (PCA)
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