Course detail

Natural Language Processing (in English)

FIT-ZPJaAcad. year: 2018/2019

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

The students will get acquainted with natural language processing and learn how to apply basic algorithms in this field. 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. They will also grasp basics of knowledge representation, inference, and relations to the artificial intelligence.
The students will learn to work in a team. They will also improve their programming skills and their knowledge of development tools.

Prerequisites

Basic knowledge of C/C++ programming or a scripting language (Perl, Python, Ruby)

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • Mid-term test - up to 9 points
  • Individual project - up to 40 points
  • Written final exam - up to 51 points

Exam prerequisites:
  • Realized individual project

Language of instruction

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.

Specification of controlled education, way of implementation and compensation for absences

The evaluation includes mid-term test, individual project, and the final exam. The mid-term test does not have a correction option, the final exam has two possible correction terms

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, winter semester, 5 credits, compulsory-optional
    branch MPV , any year of study, winter semester, 5 credits, optional
    branch MGM , any year of study, winter semester, 5 credits, optional
    branch MSK , any year of study, winter semester, 5 credits, optional
    branch MIS , any year of study, winter semester, 5 credits, optional
    branch MBS , any year of study, winter semester, 5 credits, optional
    branch MIN , any year of study, winter semester, 5 credits, optional
    branch MMM , any year of study, winter semester, 5 credits, optional

  • Programme IT-MGR-1H Master's

    branch MGH , any year of study, winter semester, 5 credits, recommended

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus


  1. Introduction, history of NLP, subdisciplines
  2. How to build a Google-like search engine, text categorization, document similarity
  3. Morphological analysis, inflective and derivational morphology, trie structure for dictionaries
  4. Syntactical analysis, constituent and dependency structures, feature structures, grammar specification formats
  5. Grammar formalisms, categorial grammars, LFG, HPSG, LTAG
  6. Methods of syntactic analysis, CKY-algorithm, chart-parsing
  7. Korpus linguistics, treebanks, TBL method
  8. Probabilistic context-free analysis, automatic alignment, machine translation
  9. Lexical semantics, dictionaries vs. encyclopedias, compositionality
  10. Transparent intensional logic for the description of meaning
  11. Pragmatics, contextual meaning relations, dynamic semantics
  12. Knowledge representation, possible-world semantics, inference
  13. The Semantic Web technologies, ontologies, OWL

Projects

26 hours, compulsory

Teacher / Lecturer

Syllabus


  • Individually assigned projects

eLearning