Covers: theory of Count-based models
Estimated time needed to finish: 20 minutes
Questions this item addresses:
  • How are the count-based models represented within the matrix?
How to use this item?

Read slides 10 through 19.

Author(s) / creator(s) / reference(s)
Dan Jurafsky
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Recipe
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Count-based models

Collaborators
Total time needed: ~2 hours
Objectives
With this list, you will learn about the count-based way of constructing vector space models (VSMs) in NLP practices
Potential Use Cases
Figuring out co-occurrence of various words in corpuses or the topic model of a specific document
Who is this for ?
BEGINNERPython beginners to machine learning
Click on each of the following annotated items to see details.
OTHER 1. Vector Semantics
  • How are the count-based models represented within the matrix?
20 minutes
OTHER 2. Improve Simple Co-Occurrence Counts
  • Are context words at different distances equally important? If not, how can we modify co-occurrence counts?
  • In language, word order is important; specifically, left and right contexts have different meanings. How can we distinguish between the left and right contexts?
30 minutes
ARTICLE 3. Creating a sparse Document Term Matrix for Topic Modeling with LDA
  • How do you create a term-document model from scratch and apply one of the common-use applications?
20 minutes

Concepts Covered

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