* class lectures

• Lecture 1 - introduction to CL course
• Lecture 2 - continue with introduction
• Lecture 3 - continue with introduction
• Lecture 4 - linguistic background
• Lecture 5 - continue with linguistic background
• Lecture 6 - NL grammar hierarchies
• Lecture 7 - parsing and context free grammars
  • Lecture 7 continued - parsing and context free grammar example
  • parsing examples
  • earley parsing
• grammars and parsing tutorials
• Lecture 8 - final word on syntax?, semantics & pragmatics
• Lecture 9 - unification based approach to NLP
• Lecture 10 - HPSG introduction
  • Lecture 10a - Muller & Sag HPSG introduction
  • |Lecture 10b - Zhang HPSG introduction
  • Lecture 10c - Naruedomkul HPSG introduction
• Lecture 11 - HPSGs - Constituent Order
  • Lecture 11 - HPSGs - Constituent Order
• Lecture 12 - Final HPSGs - examples of parts
  • English grammar
  • lexical rules
  • lexical rules 1
  • types and features
  • some universal rules
  • synopsis
    • modular hpsg
    • modular hpsg
    • just in time hpsg
    • hpsg - basic idea
    • hpsg - basic syntax
    • hpsg - consistency
    • hpsg - construction
    • hpsg - agreement
    • hpsg - dependency
    • hpsg - relative clause
    • hpsg - binding theory
    • problems with cfg's
• n-gram analysis
• Lecture 13 - Statistical NLP Introduction
  • Lecture 13+ - vector space model
  • Lecture 13++ - more vector space model
  • Parsing as Search - C, Manning

• Lecture 14 Farzana & Yasser's vector space model
  • Lecture 14 razieh’s vector space model
  • Lecture 14 abeer’s vector space model
  • Lecture 14 vector space model - morteza zihayat
  • Lecture 14 alternative vector space model
• Lecture 15 Nima & Sanjay's language modelling
• Lecture 15 elnaz text classification
• Lecture 15 Feng Gao - text classification
  • Lecture 15 alternative text classification incomplete
  • Lecture 15 alternative text classification - nadine dulisch

• Lecture 15 Babanejad-Dehaki & Yang - Parser Evaluation

• lecture_16_poots_and_saberi_hmm

• Lecture 14 Nima & Sanjay's language modelling

• Lecture 15 ameeta
• Lecture 16 haluk - probabilistic modelling and joint distribution model
• Lecture 17 nikolay - fully independent and naie bayes models
• Lecture 18 razieh - n-gram models
• Lecture 18 bahareh - n-gram models
• Lecture 18 Ross Kitsis - n-gram models
• Lecture 19 Meyer and Elasal - hidden markov models
• Lecture 19 rados - hidden markov models
• Lecture 19 leah - hidden Markov models in NLP
• Lecture 20 Bart - Bayesian belief networks
• Lecture 20 nariman - bayesian networks
• Lecture 20 Mohammed - bayesian networks
• Lecture 21 dmitri - probabilistic parsing
• Lecture 21 Vitaliy - probabilistic parsing
• Lecture 22 parser evaluation - indiana
• Lecture 22 parser evaluation & text clustering - emad ehsan
• Lecture IBM Watson videos
• Lecture nlp, etc.
• Lecture sentimap

* miscellaneous lectures

• matching
• finite state machines
• just in time subgrammar extraction
• modular hpsg
• machine translation
• decision trees
• clustering with gaussian mixtures
• syntax
• parsing
• semantics
• tagging

• statistical machine translation - esslli
  • esslli 1 - intro to statistical MT
  • esslli 2 - statistical MT, theory and praxis of decoding
  • esslli 3 - statistical MT, word alignment and phrase models
  • esslli 4 - evaluation of translation quality
  • esslli 5 - statistical MT, syntax based models