* class lectures * {{:lecture_1.pdf|Lecture 1 - introduction to CL course}} * {{:lecture_2.pdf|Lecture 2 - continue with introduction}} * {{:lecture_3.pdf|Lecture 3 - continue with introduction}} * {{:lecture_4.pdf|Lecture 4 - linguistic background}} * {{:lecture_5.pdf|Lecture 5 - continue with linguistic background}} * {{:lecture_6.pdf|Lecture 6 - NL grammar hierarchies}} * {{:lecture_7.pdf|Lecture 7 - parsing and context free grammars}} * {{:bnf1.pdf|Lecture 7 continued - parsing and context free grammar example}} * {{:parse.pdf|parsing examples}} * {{:lect10-earley_1_.pdf|earley parsing}} * {{:grammars_and_parsing_tutorial.pdf|grammars and parsing tutorials}} * {{:lecture_8.pdf|Lecture 8 - final word on syntax?, semantics & pragmatics}} * {{:lecture_unification_based_parsing.pdf|Lecture 9 - unification based approach to NLP}} * {{:lecture_9.pdf|Lecture 10 - HPSG introduction}} * {{:lsa2007-class1-intro-slides.pdf|Lecture 10a - Muller & Sag HPSG introduction}} * {{:hpsg1.pdf||Lecture 10b - Zhang HPSG introduction}} * {{:appendix_a.pdf|Lecture 10c - Naruedomkul HPSG introduction}} * {{:lecture_10.pdf|Lecture 11 - HPSGs - Constituent Order}} * {{:lsa2007-class2-order-aux-slides.pdf|Lecture 11 - HPSGs - Constituent Order}} * {{:lecture_11njc.pdf|Lecture 12 - Final HPSGs - examples of parts}} * {{:english_grammar.pdf|English grammar}} * {{:lexicalrules.pdf|lexical rules}} * {{:lexical_rules1.pdf|lexical rules 1}} * {{:types.pdf|types and features}} * {{:some_universal_principles_and_grammar_rules.pdf|some universal rules}} * {{:hpsg_a_synopsis.pdf|synopsis}} * {{:modularhpsg.ppt|modular hpsg}} * {{:modularhpsg.pdf|modular hpsg}} * {{:just-in-time.ppt|just in time hpsg}} * {{:01basic_idea.pdf|hpsg - basic idea}} * {{:02basic_systex.pdf|hpsg - basic syntax}} * {{:03consituency.pdf|hpsg - consistency}} * {{:04construction.pdf|hpsg - construction}} * {{:06agreement.pdf|hpsg - agreement}} * {{:08dependency.pdf|hpsg - dependency}} * {{:09relative_clause.pdf|hpsg - relative clause}} * {{:10binding_theory.pdf|hpsg - binding theory}} * {{:index1.pdf|problems with cfg's}} * {{:alzheimer0404n.ppt|n-gram analysis}} * {{:lecture_12_statnlp.pdf|Lecture 13 - Statistical NLP Introduction}} * {{:lecture02.ppt|Lecture 13+ - vector space model}} * {{:lecture02-1.ppt|Lecture 13++ - more vector space model}} * {{:stanford_manning14.pdf|Parsing as Search - C, Manning}} * {{:cse6339-presentation_ir_vsm.pdf|Lecture 14 Farzana & Yasser's vector space model}} * {{:vector_space_model-updated.ppt|Lecture 14 razieh’s vector space model}} * {{:cse6339_ir_-_abeer.pdf|Lecture 14 abeer’s vector space model}} * {{:presentation.ppt|Lecture 14 vector space model - morteza zihayat}} * {{:lecture_13_ir_and_vsm_.ppt|Lecture 14 alternative vector space model}} * {{:nima_and_sanjay.pdf|Lecture 15 Nima & Sanjay's language modelling}} * {{:lecture_14_text_classification.ppt|Lecture 15 elnaz text classification}} * {{:text_classification1.pdf|Lecture 15 Feng Gao - text classification}} * {{:lecture_14_text_classification_.ppt|Lecture 15 alternative text classification incomplete}} * {{:text_classification_presentation.pdf|Lecture 15 alternative text classification - nadine dulisch}} * {{|Lecture 15 Babanejad-Dehaki & Yang - Parser Evaluation}} * {{Lecture 16 Poots and Saberi HMM}} * {{:nima_and_sanjay.pdf|Lecture 14 Nima & Sanjay's language modelling}} * {{:ameeta.pdf|Lecture 15 ameeta}} * {{:lecture_16_haluk-presentation.pdf|Lecture 16 haluk - probabilistic modelling and joint distribution model}} * {{:lecture_17_fullyindependentandnaivebayesmodels-ny.pdf|Lecture 17 nikolay - fully independent and naie bayes models}} * {{:raziehn-gram_presentation.ppt|Lecture 18 razieh - n-gram models}} * {{:lecture_18_ngram_models.pps.ppt|Lecture 18 bahareh - n-gram models}} * {{:ross_kitsis.pdf|Lecture 18 Ross Kitsis - n-gram models}} * {{{{:hmm_presentation.pdf|Lecture 19 Meyer and Elasal - hidden markov models}} * {{:lecture_19_cl_-_hidden_markov_models.pdf|Lecture 19 rados - hidden markov models}} * {{:hmm.ppt|Lecture 19 leah - hidden Markov models in NLP}} * {{:bayesian_network.pdf|Lecture 20 Bart - Bayesian belief networks}} * {{:lecture_20_bayesian-networks.ppt|Lecture 20 nariman - bayesian networks}} * {{:bayesian_networks.pdf|Lecture 20 Mohammed - bayesian networks}} * {{:lecture_21_probabilisticparsing.ppt|Lecture 21 dmitri - probabilistic parsing}} * {{:2015-probabilistic-parsing.pdf|Lecture 21 Vitaliy - probabilistic parsing}} * {{:10e-eval-2x3.pdf|Lecture 22 parser evaluation - indiana}} * {{:parseval_txtcluster.pdf|Lecture 22 parser evaluation & text clustering - emad ehsan}} * {{:watson.pdf|Lecture IBM Watson videos}} * {{:document3.pdf|Lecture nlp, etc.}} * {{:civddd_oct19_006.pdf|Lecture sentimap}} * miscellaneous lectures * {{:pattern.pdf|matching}} * {{:fsa-re-2009.pdf|finite state machines}} * {{:just-in-time.ppt|just in time subgrammar extraction}} * {{:modularhpsg.ppt|modular hpsg}} * {{:kn-talk.ppt|machine translation}} * {{:dtree18.pdf|decision trees}} * {{:gmm14.pdf|clustering with gaussian mixtures}} * {{:cpe480lecture_04.ppt|syntax}} * {{:cpe480lecture_05-1.ppt|parsing}} * {{:cpe480lecture_06.ppt|semantics}} * {{:tagging_slides.pdf|tagging}} * statistical machine translation - esslli * {{:esslli-slides-day1.pdf|esslli 1 - intro to statistical MT}} * {{:esslli-slides-day2.pdf|esslli 2 - statistical MT, theory and praxis of decoding}} * {{:esslli-slides-day3.pdf|esslli 3 - statistical MT, word alignment and phrase models}} * {{:esslli-slides-day4.pdf|esslli 4 - evaluation of translation quality}} * {{:esslli-slides-day5.pdf|esslli 5 - statistical MT, syntax based models}}