IB030 Introduction to Natural Language Processing

Faculty of Informatics
Spring 2026
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
In-person direct teaching
Teacher(s)
doc. RNDr. Aleš Horák, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: doc. RNDr. Aleš Horák, Ph.D.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 40 fields of study the course is directly associated with, display
Course objectives
The course is an introduction to computational natural language processing (NLP). It introduces students to the algorithmic description of individual language levels: morphological, syntactic and semantic, to the sources of linguistic data: corpora and to current approaches based on deep learning and large language models. For each level, classical and machine learning-based approaches are compared.
Learning outcomes
Students will be able to:
- identify and summarize the main phases of computer natural language analysis;
- describe principles of algorithms used for speech analysis;
- explain the main approaches to analysis at the morphological and syntactic level of language;
- provide an overview of main language resources, their formats and processing;
- understand approaches to computational semantics and its applications.
Syllabus
  • Introduction to Computational Linguistics (Natural Language Processing, NLP).
  • Levels of description: phonetics and phonology, morphology, syntax, semantics and pragmatics.
  • Representation of morphological and syntactic structures.
  • Analysis and synthesis: speech, morphological, syntactic, semantic.
  • Computational semantics, language models.
  • Deep learning, generative models.
Literature
  • Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft, 2025). https://web.stanford.edu/~jurafsky/slp3/
  • The Oxford handbook of computational linguistics (2nd ed). Edited by Ruslan Mitkov. Oxford: Oxford University Press, 2014-2021. ISBN 9780199573691.
  • CHOMSKY, Noam. Syntaktické struktury., Logický základ teorie jazyka., O pojmu gramatické pravidlo (Syntactic Structures). 1st ed. Praha: Academia, 1966, 209 s. info
  • MATERNA, Pavel and Jan ŠTĚPÁN. Filozofická logika: nová cesta? (Philosophical logic: a new way?). Olomouc: Olomouc (Univerzita Palackého), 2000, 127 pp. ISBN 80-244-0109-6. info
Teaching methods
Lectures with real system examples, practical task.
Assessment methods
Final written test.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught every week.
Teacher's information
http://nlp.fi.muni.cz/nlp_intro/
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (recent)
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