Natural Language Processing with Python Updated EditionChapter 111
Chapter 7: Topic Modeling
Section 1 of 3-~ 2 min read-Synced from Cuantum content
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
1. 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
1. 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
1. 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
1. 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