Lectures

You can download the lecture slides here.

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  1. Introduction, Word vectors
  
  [slides]
  
• 

  2. Lab 1 Python intro
  
  [slides] [code]
  
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  3. Word vectors
  
  [slides]
  

  Suggested Readings:

  • SLP, Chapter 5, Chapter I
  • Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space
  • Pennington, Socher, & Manning (2014). GloVe: Global Vectors for Word Representation
• 

  4. Lab 2 Word vectors
  
  [slides] [code]
  
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  5. Neural Network
  
  [slides]
  

  Suggested Readings:

  • SLP, Chapter 6
• 

  6. Lab 3 Training perceptrons, PyTorch tutorial
  
  [slides] [code]
  

  More Information:

  • Numpy
  • PyTorch
• 

  7. Dependency Parsing
  
  [slides]
  

  Suggested Readings:

  • SLP, Chapter 19
  • Nivre et al. (2006). MaltParser: A Data-Driven Parser-Generator for Dependency Parsing
  • Chen & Manning (2014). A fast and accurate dependency parser using neural networks
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  8. Lab 4 Building dependency parser 1
  
  [slides] [code]
  
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  9. Language model, RNN
  
  [slides]
  

  Suggested Readings:

  • SLP, Chapter 3
  • SLP, Chapter 13
• 

  10. Lab 4 Building dependency parser 2
  
  [slides] [shared thoughts] [code]
  
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  11. LSTM
  
  [slides]
  

  Suggested Readings:

  • SLP, Chapter 13