Course on Language Technologies and NLP
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README.md

Language Technology I

A Graduate Course


Info

Instructors: Dr. Jon Dehdari and Dr. Asad Sayeed
Class Location: (Former) CiP Room, building C7.2
Class Times: Lecture: Mondays 14:00-16:00 (c.t); Lab: Wednesdays 16:00-18:00 (s.t)
Class Dates: Oct. 31st - Feb. 15th
Jon's Offices: either room 1.15, building A2.2, or room 1.11 building D3.1
Asad's Office: room 3.04, building C7.4
Final exam: Feb. 22nd, 16:00 - 18:00, Conference Room of Building C7.4

Purpose

Language Technologies I teaches the theoretical foundation of modern computational linguistics and natural language processing. This includes important machine learning techniques.

Outline

  1. Formal models of language: possibilities (homework)
  2. Statistical models of language: probabilities (homework)
  3. Applications of language models
  4. n-gram language models and smoothing (more info) (homework) (training data) (testing data) (example transcript) (Bad-Turing transcript) (lazy tokenization script)
  5. Parts of speech, word clusters, and class-based language models
  6. Log-linear models (homework)
  7. Word vectors, and applications (homework)
  8. Feedforward neural networks and autoencoders (homework)
  9. Recurrent neural networks and their language models (homework) (example transcript)
  10. Probabilistic context-free grammars, parsing, and syntactic language models (homework - small updates)
  11. Sequence-to-sequence models and neural machine translation
  12. Convolutional networks and character-based models of language

External Links