CSE6339

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* 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 5 continued - regular expressions and finite state automata

• Lecture 6 - NL grammar hierarchies
• Lecture 7 - parsing and context free grammars
• Lecture 8 - final word on syntax?, semantics & pragmatics
• Lecture 9 - unification-based approach to NLP
• Lecture 10 - unification review, HPSG introduction, principles, rules, examples, modularity
• Lecture 11 - HPSGs - How its done, Examples, Examples, Examples
• Lecture 12 - Final HPSGs - Cleaning up and final aspects, semantics, overview to statistical NLP
• Lecture 13 razieh’s vector space model
• Lecture 13 alternative vector space model
• Lecture 14 elnaz text classification
• Lecture 14 alternative text classification incomplete
• 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 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 21 dmitri - probabilistic parsing

* miscellaneous lectures

• 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