Faculty of Information Technology, BUT

Course details

Natural Language Processing

ZPJ Acad. year 2015/2016 Winter semester 5 credits

Course is not open in this year
Close
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

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

51 exam, 10 half-term test, 39 projects

Department

Lecturer

Subject specific learning outcomes and competences

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.

Generic learning outcomes and competences

The students will learn to work in a team. They will also improve their programming skills and their knowledge of development tools.

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 kwnowledge and skills

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

Study literature

  • Manning, C. D., Schütze, H., Foundations of Statistical Natural Language Processing, MIT Press, 1999, ISBN 0-262-13360-1.

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 lectures

  1. Introduction, history of NLP, subdisciplines
  2. How to build a Google-like search engine, text categorization, document similarity
  3. Morphological analysis, inflective and derivation 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

Syllabus - others, projects and individual work of students

  • Individually assigned projects

Progress assessment

  • Mid-term test - up to 10 points
  • Individual project - up to 39 points
  • Written final exam - up to 51 points

Controlled instruction

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.

Exam prerequisites

  • Realized individual project

Course inclusion in study plans

Back to top