nlp1.pdf

Natural Language Processing MCQs 1. Which of the following is not a task in natural language processing? a) Sentiment analysis b) Speech recognition c) Image classification d) Named entity recognition Answer: c) Image classification 2. What is the primary goal of natural language p...

Natural Language Processing MCQs 1. Which of the following is not a task in natural language processing? a) Sentiment analysis b) Speech recognition c) Image classification d) Named entity recognition Answer: c) Image classification 2. What is the primary goal of natural language processing? a) Understanding and generating human language b) Translating languages c) Analyzing data patterns d) Creating conversational agents Answer: a) Understanding and generating human language 3. Which of the following techniques is commonly used in text classification tasks? a) Latent Semantic Analysis (LSA) b) Convolutional Neural Networks (CNN) c) Principal Component Analysis (PCA) d) Support Vector Machines (SVM) Answer: d) Support Vector Machines (SVM) 4. What is the process of converting words into their base or root form called? a) Tokenization b) Stemming c) Lemmatization d) Part-of-speech tagging Answer: b) Stemming 5. Which algorithm is commonly used in named entity recognition? a) K-means clustering b) Hidden Markov Models (HMM) c) Apriori algorithm d) Decision trees Answer: b) Hidden Markov Models (HMM) 6. What is the purpose of the Bag-of-Words (BoW) model in NLP? a) To represent words as vectors b) To calculate word frequencies c) To identify syntactic dependencies d) To perform sentiment analysis Answer: b) To calculate word frequencies 7. Which NLP library in Python provides a comprehensive set of tools for natural language processing? a) TensorFlow b) PyTorch c) NLTK (Natural Language Toolkit) d) Scikit-learn Answer: c) NLTK (Natural Language Toolkit) 8. Which of the following is an example of a stop word? a) Noun b) Verb c) Adjective d) The Answer: d) The 9. Which technique is used to predict the probability of a sequence of words in a given context? a) Language modeling b) Named entity recognition c) Sentiment analysis d) Machine translation Answer: a) Language modeling 10. Which NLP task involves labeling words in a sentence with their respective grammatical categories? a) Named entity recognition b) Part-of-speech tagging c) Dependency parsing d) Sentiment analysis Answer: b) Part-of-speech tagging 11. Which neural network architecture is commonly used for sequence-to-sequence tasks like machine translation? a) Long Short-Term Memory (LSTM) b) Convolutional Neural Network (CNN) c) Recurrent Neural Network (RNN) d) Transformer Answer: d) Transformer 12. Which of the following algorithms is used for topic modeling? a) K-means clustering b) Naive Bayes c) Latent Dirichlet Allocation (LDA) d) Random Forests Answer: c) Latent Dirichlet Allocation (LDA) 13. Which technique aims to identify and extract the main ideas or topics from a collection of documents? a) Sentiment analysis b) Text summarization c) Named entity recognition d) Document clustering Answer: b) Text summarization 14. Which metric is commonly used to evaluate machine translation systems? a) BLEU score b) F1 score c) Precision d) Recall Answer: a) BLEU score 15. Which of the following is an example of a word embedding technique? a) One-Hot Encoding b) Bag-of-Words c) Latent Semantic Analysis (LSA) d) Word2Vec Answer: d) Word2Vec 16. Which method is used to calculate the similarity between two documents based on their content? a) Cosine similarity b) Euclidean distance c) Jaccard similarity d) Pearson correlation coebicient Answer: a) Cosine similarity 17. Which technique is used to generate new sentences or text based on existing data? a) Sentiment analysis b) Text generation c) Named entity recognition d) Part-of-speech tagging Answer: b) Text generation 18. Which algorithm is commonly used for sentiment analysis? a) Naive Bayes b) K-nearest neighbors (KNN) c) Decision trees d) Support Vector Machines (SVM) Answer: a) Naive Bayes 19. What is the purpose of the attention mechanism in neural networks? a) To improve computational ebiciency b) To reduce overfitting c) To focus on relevant information d) To calculate feature importance Answer: c) To focus on relevant information 20. Which of the following is not a sequence labeling task? a) Named entity recognition b) Part-of-speech tagging c) Sentiment analysis d) Chunking Answer: c) Sentiment analysis 21. Which technique is used to identify the syntactic structure of a sentence by analyzing the relationships between words? a) Dependency parsing b) Sentiment analysis c) Text classification d) Named entity recognition Answer: a) Dependency parsing 22. Which method is used to deal with the problem of out-of-vocabulary words in language modeling? a) Word sense disambiguation b) WordNet c) Byte Pair Encoding (BPE) d) Named entity recognition Answer: c) Byte Pair Encoding (BPE) 23. Which technique is used to improve the performance of machine translation models by leveraging monolingual data? a) Transfer learning b) Reinforcement learning c) Data augmentation d) Backtranslation Answer: d) Backtranslation 24. Which of the following is a popular pre-trained language model developed by OpenAI? a) BERT b) Word2Vec c) GloVe d) ElMo Answer: a) BERT 25. Which method is used to break down a sentence into its grammatical components? a) Chunking b) Lemmatization c) Stemming d) Tokenization Answer: a) Chunking 26. Which technique is used to generate word representations based on the co- occurrence patterns of words in a large corpus? a) Word sense disambiguation b) Named entity recognition c) Latent Semantic Analysis (LSA) d) Text summarization Answer: c) Latent Semantic Analysis (LSA) 27. Which of the following is an example of a deep learning model architecture used in NLP? a) Random Forests b) Support Vector Machines (SVM) c) Bidirectional LSTM d) K-means clustering Answer: c) Bidirectional LSTM 28. Which technique is used to handle imbalanced datasets in text classification? a) Oversampling b) Undersampling c) SMOTE (Synthetic Minority Over-sampling Technique) d) All of the above Answer: d) All of the above 29. Which method is used to assign a sentiment label to a given text? a) Named entity recognition b) Sentiment analysis c) Part-of-speech tagging d) Dependency parsing Answer: b) Sentiment analysis 30. Which technique is used to identify and extract specific pieces of information from unstructured text? a) Sentiment analysis b) Text classification c) Named entity recognition d) Word sense disambiguation Answer: c) Named entity recognition Natural Language Processing MCQs 1. Which of the following is not a task in natural language processing? a) Sentiment analysis b) Speech recognition c) Image classification d) Named entity recognition Answer: c) Image classification 2. What is the primary goal of natural language processing? a) Understanding and generating human language b) Translating languages c) Analyzing data patterns d) Creating conversational agents Answer: a) Understanding and generating human language 3. Which of the following techniques is commonly used in text classification tasks? a) Latent Semantic Analysis (LSA) b) Convolutional Neural Networks (CNN) c) Principal Component Analysis (PCA) d) Support Vector Machines (SVM) Answer: d) Support Vector Machines (SVM) 4. What is the process of converting words into their base or root form called? a) Tokenization b) Stemming c) Lemmatization d) Part-of-speech tagging Answer: b) Stemming 5. Which algorithm is commonly used in named entity recognition? a) K-means clustering b) Hidden Markov Models (HMM) c) Apriori algorithm d) Decision trees Answer: b) Hidden Markov Models (HMM) 6. What is the purpose of the Bag-of-Words (BoW) model in NLP? a) To represent words as vectors b) To calculate word frequencies c) To identify syntactic dependencies d) To perform sentiment analysis Answer: b) To calculate word frequencies 7. Which NLP library in Python provides a comprehensive set of tools for natural language processing? a) TensorFlow b) PyTorch c) NLTK (Natural Language Toolkit) d) Scikit-learn Answer: c) NLTK (Natural Language Toolkit) 8. Which of the following is an example of a stop word? a) Noun b) Verb c) Adjective d) The Answer: d) The 9. Which technique is used to predict the probability of a sequence of words in a given context? a) Language modeling b) Named entity recognition c) Sentiment analysis d) Machine translation Answer: a) Language modeling 10. Which NLP task involves labeling words in a sentence with their respective grammatical categories? a) Named entity recognition b) Part-of-speech tagging c) Dependency parsing d) Sentiment analysis Answer: b) Part-of-speech tagging 11. Which neural network architecture is commonly used for sequence-to-sequence tasks like machine translation? a) Long Short-Term Memory (LSTM) b) Convolutional Neural Network (CNN) c) Recurrent Neural Network (RNN) d) Transformer Answer: d) Transformer 12. Which of the following algorithms is used for topic modeling? a) K-means clustering b) Naive Bayes c) Latent Dirichlet Allocation (LDA) d) Random Forests Answer: c) Latent Dirichlet Allocation (LDA) 13. Which technique aims to identify and extract the main ideas or topics from a collection of documents? a) Sentiment analysis b) Text summarization c) Named entity recognition d) Document clustering Answer: b) Text summarization 14. Which metric is commonly used to evaluate machine translation systems? a) BLEU score b) F1 score c) Precision d) Recall Answer: a) BLEU score 15. Which of the following is an example of a word embedding technique? a) One-Hot Encoding b) Bag-of-Words c) Latent Semantic Analysis (LSA) d) Word2Vec Answer: d) Word2Vec 16. Which method is used to calculate the similarity between two documents based on their content? a) Cosine similarity b) Euclidean distance c) Jaccard similarity d) Pearson correlation coebicient Answer: a) Cosine similarity 17. Which technique is used to generate new sentences or text based on existing data? a) Sentiment analysis b) Text generation c) Named entity recognition d) Part-of-speech tagging Answer: b) Text generation 18. Which algorithm is commonly used for sentiment analysis? a) Naive Bayes b) K-nearest neighbors (KNN) c) Decision trees d) Support Vector Machines (SVM) Answer: a) Naive Bayes 19. What is the purpose of the attention mechanism in neural networks? a) To improve computational ebiciency b) To reduce overfitting c) To focus on relevant information d) To calculate feature importance Answer: c) To focus on relevant information 20. Which of the following is not a sequence labeling task? a) Named entity recognition b) Part-of-speech tagging c) Sentiment analysis d) Chunking Answer: c) Sentiment analysis 21. Which technique is used to identify the syntactic structure of a sentence by analyzing the relationships between words? a) Dependency parsing b) Sentiment analysis c) Text classification d) Named entity recognition Answer: a) Dependency parsing 22. Which method is used to deal with the problem of out-of-vocabulary words in language modeling? a) Word sense disambiguation b) WordNet c) Byte Pair Encoding (BPE) d) Named entity recognition Answer: c) Byte Pair Encoding (BPE) 23. Which technique is used to improve the performance of machine translation models by leveraging monolingual data? a) Transfer learning b) Reinforcement learning c) Data augmentation d) Backtranslation Answer: d) Backtranslation 24. Which of the following is a popular pre-trained language model developed by OpenAI? a) BERT b) Word2Vec c) GloVe d) ElMo Answer: a) BERT 25. Which method is used to break down a sentence into its grammatical components? a) Chunking b) Lemmatization c) Stemming d) Tokenization Answer: a) Chunking 26. Which technique is used to generate word representations based on the co- occurrence patterns of words in a large corpus? a) Word sense disambiguation b) Named entity recognition c) Latent Semantic Analysis (LSA) d) Text summarization Answer: c) Latent Semantic Analysis (LSA) 27. Which of the following is an example of a deep learning model architecture used in NLP? a) Random Forests b) Support Vector Machines (SVM) c) Bidirectional LSTM d) K-means clustering Answer: c) Bidirectional LSTM 28. Which technique is used to handle imbalanced datasets in text classification? a) Oversampling b) Undersampling c) SMOTE (Synthetic Minority Over-sampling Technique) d) All of the above Answer: d) All of the above 29. Which method is used to assign a sentiment label to a given text? a) Named entity recognition b) Sentiment analysis c) Part-of-speech tagging d) Dependency parsing Answer: b) Sentiment analysis 30. Which technique is used to identify and extract specific pieces of information from unstructured text? a) Sentiment analysis b) Text classification c) Named entity recognition d) Word sense disambiguation Answer: c) Named entity recognition