Course details

Natural Language Processing

ZPD Acad. year 2017/2018 Winter semester

Current academic year

Foundations of the natural language processing, language data in corpora, levels of description: phonetics and phonology, morphology, syntax, semantics and pragmatics. Traditional vs. formal grammars: representation of morphological and syntactic structures, meaning representation. context-free grammars and their context-sensitive extensions, DCG (Definite Clause Grammars), CKY algorithm (Cocke-Kasami-Younger), chart-parsing. Problem of ambiguity. Electronic dictionaries: representation of lexical knowledge. Types of the machine readable dictionaries. Semantic representation of sentence meaning. The Compositionality Principle, composition of meaning. Semantic classification: valency frames, predicates, ontologies, transparent intensional logic (TIL) and its application to semantic analysis of sentences. Pragmatics: semantic and pragmatic nature of noun groups, discourse structure, deictic expressions, verbal and non-verbal contexts. Natural language understanding: semantic representation, inference and knowledge representations.

Guarantor

Smrž Pavel, doc. RNDr., Ph.D. (DCGM)

Language of instruction

Czech, English

Completion

Examination (oral)

Assessment points

  • 100 pts final exam (written part)

Department

Department of Computer Graphics and Multimedia (UPGM)

Subject specific learning outcomes and competences

Students will get acquainted with advanced methods of natural language processing. They will understand the algorithmic description of the main language levels: morphology, syntax, semantics, and pragmatics, as well as the resources of natural language data - corpora. By means of a self-study and a consultation, they will also grasp detailed knowledge of a selected part of the NLP field.

Learning objectives

To understand natural language processing and to learn how to apply basic algorithms in this field. To get acquainted with the algorithmic description of the main language levels: morphology, syntax, semantics, and pragmatics, as well as the resources of natural language data - corpora. To conceive basics of knowledge representation, inference, and relations to the artificial intelligence.

 

Prerequisite knowledge and skills

There are no prerequisites

Study literature

  • Hajičová, E., Panevová, J., Sgall, P., Úvod do teoretické a počítačové lingvistiky, sv. I., Teoretická lingvistika, Praha: Karolinum, 2002, ISBN 8024604701.
  • Mařík,V., Štěpánková,O., Lažanský, J. a kol.: Umělá inteligence (4), ACADEMIA Praha, 2003, ISBN 80-200-1044-0.

Fundamental literature

  • Allen, J., Natural language understanding. 2nd ed. Redwood City : Benjamin/Cummings Publishing Company, 1995. ISBN 0-8053-0334-0.
  • Manning, C. D., Schütze, H., Foundations of Statistical Natural Language Processing, MIT Press, 1999, ISBN 0-262-13360-1.

Syllabus of seminars

Syllabus of lectures:
  1. Advanced methods of  text categorization, document similarity
  2. Morphological analysis, inflective and derivational morphology, trie structure for dictionaries
  3. Methods of syntactic analysis for language modeling
  4. Probabilistic context-free analysis, automatic alignment, machine translation
  5. Lexical semantics, dictionaries vs. encyclopedias, compositionality
  6. The Semantic Web technologies, ontologies, OWL

Progress assessment

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Controlled instruction

There are no checked study.

Course inclusion in study plans

  • Programme VTI-DR-4, field DVI4, any year of study, Elective
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