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Natural language processing

Course Instructor: Stefan Trausan-Matu

Syllabus:

  • Introduction in Natural Language Processing.Phonetics and phonology.
  • Finite state transducers, two level morphology, paradigmatic morphology, Stemming and lemmatization.
  • Corpus linguistics.
  • Hidden Markov Models;
  • Naïve Bayes method with applications in NLP.
  • Different classes of grammatical formalisms for natural language.
  • Unification grammars, chart parsing, Earley and CKY algorithms.
  • Part of Speech Tagging Case grammars, Ontologies, Sense disambiguation.
  • LSA, pLSA, LDA.
  • Pragmatics and discourse analysis.
  • Coreferences.
  • Rhetorical schemas and natural language generation.
  • Polyphonic theory.
  • Conversation analysis