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Natural Language Processing (NLP)

NLP involves the interaction between computers and human language, enabling machines to understand, interpret, and generate human-like text. Applications range from sentiment analysis and language translation to chatbots and voice recognition systems.

Which model uses masked language modelling?
  1. A-GPT
  2. B-BERT
  3. C-ELMo
  4. D-ULMFit
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  6. Data Science / Natural Language Processing (NLP)
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Which metric evaluates POS tagging?
  1. A-Accuracy
  2. B-BLEU
  3. C-ROUGE
  4. D-F1
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Which tokenizer handles subwords best?
  1. A-Whitespace
  2. B-WordPunct
  3. C-BPE
  4. D-Regex
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Which metric is commonly used to evaluate machine translation in NLP?
  1. A-Precision
  2. B-Recall
  3. C-BLEU Score
  4. D-F1 Score
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  6. Data Science / Natural Language Processing (NLP)
What is the purpose of Named Entity Recognition (NER) in NLP?
  1. A-Identifying relationships between entities
  2. B-Assigning sentiment to text
  3. C-Recognizing and classifying entities in text
  4. D-Tokenizing sentences
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Which NLP task involves determining the relationship between words in a sentence?
  1. A-Named Entity Recognition (NER)
  2. B-Relationship Extraction
  3. C-Sentiment Analysis
  4. D-Tokenization
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In the context of NLP, what does POS tagging stand for?
  1. A-Position of Speech tagging
  2. B-Part of Speech tagging
  3. C-Power of Syntax tagging
  4. D-Processing of Semantics tagging
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Which of the following is an example of a syntactic ambiguity in NLP?
  1. A-Bank of the river
  2. B-Bank where you deposit money
  3. C-Apple fruit
  4. D-Apple Inc.
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  6. Data Science / Natural Language Processing (NLP)
What is the purpose of Lemmatization in NLP?
  1. A-Identifying named entities
  2. B-Reducing words to their base or root form
  3. C-Classifying text into categories
  4. D-Translating text to another language
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Which library in Python is commonly used for NLP tasks?
  1. A-TensorFlow
  2. B-PyTorch
  3. C-NLTK (Natural Language Toolkit)
  4. D-Scikit-learn
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What is Tokenization in NLP?
  1. A-Process of converting tokens to text
  2. B-Process of dividing text into words or phrases
  3. C-Converting text to binary code
  4. D-Analyzing sentiments in text
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Which of the following is a subtask of NLP?
  1. A-Image Recognition
  2. B-Speech Synthesis
  3. C-Sentiment Analysis
  4. D-Object Detection
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What does NLP stand for?
  1. A-Natural Learning Process
  2. B-Neural Language Processing
  3. C-Natural Language Processing
  4. D-Networked Linguistic Pattern
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How does keyword extraction in NLP contribute to SEO?
  1. A-Reducing website load times
  2. B-Identifying and extracting relevant keywords from text
  3. C-Enhancing image alt tags
  4. D-Improving website navigation
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  6. Data Science / Natural Language Processing (NLP)
What is the primary challenge addressed by natural language generation (NLG) in NLP?
  1. A-Analyzing user behavior
  2. B-Generating human-like text from structured data
  3. C-Reducing the number of outbound links
  4. D-Enhancing website design
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How can NLP contribute to improving user experience on a website?
  1. A-Increasing image file sizes
  2. B-Personalizing content based on user interactions and preferences
  3. C-Reducing server response time
  4. D-Utilizing high-resolution images
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What is the role of word embeddings in NLP?
  1. A-Identifying stop words
  2. B-Converting words into their base form
  3. C-Representing words as dense vectors in a continuous vector space
  4. D-Enhancing website security
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What NLP technique is used for breaking down text into smaller units, such as words or phrases?
  1. A-Tokenization
  2. B-Lemmatization
  3. C-Clustering
  4. D-Regression
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How can sentiment analysis in NLP be beneficial for SEO?
  1. A-Optimizing meta descriptions
  2. B-Increasing server response time
  3. C-Enhancing website layout
  4. D-Identifying and understanding user sentiment towards content
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Which machine learning algorithm is commonly used for text classification in NLP?
  1. A-K-Means Clustering
  2. B-Decision Trees
  3. C-Support Vector Machines (SVM)
  4. D-Principal Component Analysis (PCA)
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What is the primary purpose of named entity recognition (NER) in NLP?
  1. A-Extracting sentiment from text
  2. B-Identifying and classifying entities such as names, locations, and organizations
  3. C-Reducing the dimensionality of text data
  4. D-Improving website speed
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Which NLP technique is commonly used to convert words into their base or root form?
  1. A-Tokenization
  2. B-Lemmatization
  3. C-Named Entity Recognition (NER)
  4. D-Sentiment Analysis
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What is the primary objective of natural language processing (NLP)?
  1. A-Enhancing website aesthetics
  2. B-Enabling computers to understand, interpret, and generate human-like text
  3. C-Improving server performance
  4. D-Reducing image file sizes
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