NLP: Word Embedding and Document Terms
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Questions and Answers

What is the primary purpose of word embedding in NLP?

  • To train hand-built models that use graph embeddings
  • To represent words as graphs
  • To represent words for text analysis in the form of real-valued vectors (correct)
  • To create a corpus of documents
  • What is a document in the context of NLP?

  • A feature in the corpus
  • A single text data point (correct)
  • A binary value in a classification task
  • A collection of all the documents present in our dataset
  • What is the collection of all the documents present in our dataset called?

  • A classification task
  • A document
  • A feature
  • A corpus (correct)
  • What is the target variable in the classification task of predicting which tweets are about real disasters and which ones are not?

    <p>The binary values (1: Real Disaster, 0: Not real Disaster)</p> Signup and view all the answers

    What are the unique words in the corpus considered as?

    <p>Features</p> Signup and view all the answers

    What is the primary reason why Machine Learning and Deep Learning algorithms require numeric input?

    <p>Because they require numerical numbers to perform tasks</p> Signup and view all the answers

    Why do we need word embeddings in Machine Learning and Deep Learning?

    <p>To extract knowledge from text data and build useful applications</p> Signup and view all the answers

    What is the main difference between Frequency-based and Prediction-based Word Embeddings?

    <p>One is based on frequency, the other on prediction</p> Signup and view all the answers

    Which of the following is an example of Frequency-based Word Embedding?

    <p>Counter Vector</p> Signup and view all the answers

    What is the benefit of using word embeddings in Machine Learning and Deep Learning?

    <p>It enables the extraction of knowledge from text data</p> Signup and view all the answers

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