PLIN037 Semantic Computing

Faculty of Arts
Spring 2025
Extent and Intensity
0/2/0. 4 credit(s). Type of Completion: z (credit).
Taught partially online.
Teacher(s)
RNDr. Zuzana Nevěřilová, Ph.D. (lecturer)
Guaranteed by
RNDr. Zuzana Nevěřilová, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
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.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30
fields of study / plans the course is directly associated with
Course objectives
At the end of the course, students should be able to understand terms from the field of computer natural language processing on the semantic level; understand multi-annotator issues and inter-annotator agreement; use existing knowledge bases and know their advantages and drawbacks; understand the term linked open data; use the languages of the Semantic web; understand semantic networks; understand and explain the principle of inference in semantic networks; interpret probabilistic models of meaning; understand semantic modeling using word embeddings; understand and explain anaphora resolution issues and algorithms for automatic anaphora resolution; understand the discourse analysis
Learning outcomes
The student will be able:
- to identify a natural language processing problem as a problem of semantic processing,
- propose an annotation scheme and evaluation methods,
- select and apply a particular knowledge base on a particular problem,
- select and apply a particular language model on a particular problem,
- detect bottleneck of a particular processing.
Syllabus
  • 1. Seeking of the meaning: ambiguity on different levels of language analysis.
  • 2. Semantic annotation: inter-annotator agreement.
  • 3. Formal knowledge representation: selectional restrictions, semantic features, semantic networks and frames, psychological experiments.
  • 4. Semantic relations between clauses and sentences, implication and entailment, inference.
  • 5. Formal languages for knowledge representation: RDF, OWL. Linked open data.
  • 6. Probabilistic models.
  • 6. Word embeddings. Pre-trained models.
  • 7. Discourse analysis, anaphora resolution.
Literature
    recommended literature
  • GODDARD, Cliff. Semantic Analysis : a practical introduction. Oxford: Oxford University Press, 1998, xv, 411 s. ISBN 0-19-870017-2. info
Teaching methods
Lectures, and learning materials in the IS (presentations, videos in Czech). Hands-on seminar, in form of Jupyter Notebook in Colab (no installed software needed).
Assessment methods
Presentation of a scientific article or a current topic related to the field (student's choice).
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught: every week.
The course is also listed under the following terms Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (Spring 2025, recent)
  • Permalink: https://is.muni.cz/course/phil/spring2025/PLIN037