Course detail

Natural Language Processing

FIT-ZPJaAcad. year: 2017/2018

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

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

  • 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

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

  • Realized individual project

Language of instruction

English

Work placements

Not applicable.

Course curriculum

    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 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

    Syllabus - others, projects and individual work of students:
    • Individually assigned projects

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, elective
    branch MGM , any year of study, winter semester, 5 credits, elective
    branch MSK , any year of study, winter semester, 5 credits, elective
    branch MIS , any year of study, winter semester, 5 credits, elective
    branch MBS , any year of study, winter semester, 5 credits, elective
    branch MIN , any year of study, winter semester, 5 credits, elective
    branch MMM , any year of study, winter semester, 5 credits, elective

  • Programme IT-MGR-1H Master's

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