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Better Statistical Natural Language Processing assignments

Join a growing community of educators who are transforming the way they teach Statistical Natural Language Processing. Save time and improve learning outcomes by incorporating our full set of adaptive review assignments formatted to optimize long-term learning outcomes.
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46.7
Explain how a bag-of-words (BoW) model can be used for text categorization. Provide an example to illustrate your explanation.
Instructor solution
Learning resources
Instructor answered
The BoW model represents text by converting it into a vector of word frequencies, enabling categorization through algorithms that identify patterns in word usage across documents. For example, in sentiment analysis, frequent positive words in positive reviews help classify them as such.
You answered
BoW converts text to vector; classify using ML algorithms.

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Class Outline

  • 15
    units
  • 15
    adaptive reviews
Week 1: Introduction and Text Categorization
  • Week 1: Introduction and Text Categorization
  • Week 2: Machine Learning for Text Categorization (Part 1)
  • Week 3: Machine Learning for Text Categorization (Part 2)
  • Week 4: Distributional Similarity and Word Embeddings
  • Week 5: Probability Theory (Part 1)
  • Week 6: Probability Theory (Part 2)
  • Week 7: N-gram Models
  • Week 8: Midterm Review and Exam
  • Week 9: Sequence Models (Part 1)
  • Week 10: Sequence Models (Part 2)
  • Week 11: Structured Learning (Part 1)
  • Week 12: Structured Learning (Part 2)
  • Week 13: Alignment Models (Part 1)
  • Week 14: Alignment Models (Part 2)
  • Week 15: Advanced Techniques
  • Week 1: Introduction and Text Categorization
    Review Assignment
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46.7
Given a dataset of text documents, you have trained a Naive Bayes classifier to categorize the documents into different topics. Describe how you would evaluate the performance of this probabilistic model. Include at least three different evaluation metrics in your answer.
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52.8
Write a function to multiply two input parameters, a and b.
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def product_of_two(a, b):  # Your code here!

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