DZPJUI Advanced Methods for Natural Language Processing and Artificial Intelligence

Faculty of Informatics
Autumn 2014
Extent and Intensity
3/0. 3 credit(s). Type of Completion: z (credit).
prof. PhDr. Karel Pala, CSc. (lecturer)
doc. RNDr. Ivan Kopeček, CSc. (lecturer)
doc. RNDr. Petr Sojka, Ph.D. (lecturer)
doc. RNDr. Aleš Horák, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Antonín Kučera, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Informatics
! NOWANY ( DEMBSY Embedded systems , DFOME Formal Methods , DMKZI Quantum Information Processing , DPGZO Graphics & Image Processing , DMPOS Computer Networks Methods , DMZDD Digital Data Processing , DPITS EIT Systems and Services , DPOSO Advances in Concurrency , DRPSEC Research in )
Complete MA study
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
there are 8 fields of study the course is directly associated with, display
Course objectives
The course integrates the individual researcg activities of the students in a broader context of the NLP Cente. The main goal of the course is research and presentation activities of the students. The team collaboration is strongly emphasized. The students join the projects of the Centre with the goal to take part in the international research structures, like Nets of Excelence or other EU research programs.
  • The main topics solved in the NLP Centre include:
  • - Computer understanding of natural language.
  • - Morphology, syntax, semantics.
  • - Text corpora, disambiguation, statistical systems.
  • - Representation of morphological structures, algorithms of morphological analysis.
  • - Representation of the syntactical structures.
  • - Semantical represention, lexical algorithms, elektronical dictionaries.
  • - Semantics, normal translational algorithm.
  • - Pragmatics, discourse analysis, segmentation, anaphora, coreference.
  • - Dialogue systems.
  • - Applications of DS in assistive technologies.
  • - Inference and knowledge representation.
  • - Communication agents.
  • - Evaluation techniques.
  • - Machine learning.
  • - AI and NLP techniques.
  • Literaturu určuje školitel individuálně.
  • Supervisor determines the literature individually.
Teaching methods
Research and presentation activities of the students, team collaboration, joining the research projects.
Assessment methods
Evaluation of the research and presentation activity of the student.
Language of instruction
Further comments (probably available only in Czech)
The course is taught each semester.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2009, Spring 2010, Autumn 2010, Spring 2011, Autumn 2011, Spring 2012, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, Autumn 2017, Spring 2018, Autumn 2018, Spring 2019, Autumn 2019, Spring 2020, Autumn 2020.
  • Enrolment Statistics (Autumn 2014, recent)
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