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Natural Language Processing with Python Updated Edition

Quiz Part III: Topic Modeling and Text Summarization

Chapter 7: Topic Modeling

  1. What is the primary goal of topic modeling?
    • A) To classify text into predefined categories
    • B) To identify the underlying themes or topics in a collection of documents
    • C) To generate summaries of text
    • D) To translate text from one language to another
  2. Which of the following techniques is based on singular value decomposition (SVD)?
    • A) Latent Dirichlet Allocation (LDA)
    • B) Hierarchical Dirichlet Process (HDP)
    • C) Latent Semantic Analysis (LSA)
    • D) TextRank
  3. What is a key advantage of Hierarchical Dirichlet Process (HDP) over Latent Dirichlet Allocation (LDA)?
    • A) HDP is simpler to implement
    • B) HDP automatically determines the number of topics
    • C) HDP requires less computational resources
    • D) HDP provides more interpretable results
  4. Which library did we use to implement Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) in Python?
    • A) NLTK
    • B) spaCy
    • C) Gensim
    • D) Scikit-learn
  5. In LDA, what does the term 'topic-word distribution' refer to?
    • A) The probability distribution of words in a document
    • B) The probability distribution of topics in a document
    • C) The probability distribution of words given a topic
    • D) The probability distribution of topics given a word

Chapter 7: Topic Modeling

  1. What is the primary goal of topic modeling?
    • A) To classify text into predefined categories
    • B) To identify the underlying themes or topics in a collection of documents
    • C) To generate summaries of text
    • D) To translate text from one language to another
  2. Which of the following techniques is based on singular value decomposition (SVD)?
    • A) Latent Dirichlet Allocation (LDA)
    • B) Hierarchical Dirichlet Process (HDP)
    • C) Latent Semantic Analysis (LSA)
    • D) TextRank
  3. What is a key advantage of Hierarchical Dirichlet Process (HDP) over Latent Dirichlet Allocation (LDA)?
    • A) HDP is simpler to implement
    • B) HDP automatically determines the number of topics
    • C) HDP requires less computational resources
    • D) HDP provides more interpretable results
  4. Which library did we use to implement Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) in Python?
    • A) NLTK
    • B) spaCy
    • C) Gensim
    • D) Scikit-learn
  5. In LDA, what does the term 'topic-word distribution' refer to?
    • A) The probability distribution of words in a document
    • B) The probability distribution of topics in a document
    • C) The probability distribution of words given a topic
    • D) The probability distribution of topics given a word

Chapter 7: Topic Modeling

  1. What is the primary goal of topic modeling?
    • A) To classify text into predefined categories
    • B) To identify the underlying themes or topics in a collection of documents
    • C) To generate summaries of text
    • D) To translate text from one language to another
  2. Which of the following techniques is based on singular value decomposition (SVD)?
    • A) Latent Dirichlet Allocation (LDA)
    • B) Hierarchical Dirichlet Process (HDP)
    • C) Latent Semantic Analysis (LSA)
    • D) TextRank
  3. What is a key advantage of Hierarchical Dirichlet Process (HDP) over Latent Dirichlet Allocation (LDA)?
    • A) HDP is simpler to implement
    • B) HDP automatically determines the number of topics
    • C) HDP requires less computational resources
    • D) HDP provides more interpretable results
  4. Which library did we use to implement Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) in Python?
    • A) NLTK
    • B) spaCy
    • C) Gensim
    • D) Scikit-learn
  5. In LDA, what does the term 'topic-word distribution' refer to?
    • A) The probability distribution of words in a document
    • B) The probability distribution of topics in a document
    • C) The probability distribution of words given a topic
    • D) The probability distribution of topics given a word

Chapter 7: Topic Modeling

  1. What is the primary goal of topic modeling?
    • A) To classify text into predefined categories
    • B) To identify the underlying themes or topics in a collection of documents
    • C) To generate summaries of text
    • D) To translate text from one language to another
  2. Which of the following techniques is based on singular value decomposition (SVD)?
    • A) Latent Dirichlet Allocation (LDA)
    • B) Hierarchical Dirichlet Process (HDP)
    • C) Latent Semantic Analysis (LSA)
    • D) TextRank
  3. What is a key advantage of Hierarchical Dirichlet Process (HDP) over Latent Dirichlet Allocation (LDA)?
    • A) HDP is simpler to implement
    • B) HDP automatically determines the number of topics
    • C) HDP requires less computational resources
    • D) HDP provides more interpretable results
  4. Which library did we use to implement Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) in Python?
    • A) NLTK
    • B) spaCy
    • C) Gensim
    • D) Scikit-learn
  5. In LDA, what does the term 'topic-word distribution' refer to?
    • A) The probability distribution of words in a document
    • B) The probability distribution of topics in a document
    • C) The probability distribution of words given a topic
    • D) The probability distribution of topics given a word