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