Course detail

Natural Language Processing

FIT-ZPDAcad. year: 2019/2020

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.

Learning outcomes of the course unit

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.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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.
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.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

Czech, English

Work placements

Not applicable.

Aims

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.

 

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, 0 credits, optional

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Syllabus


  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

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