FI:PA153 NL Processing - Course Information
PA153 Natural Language ProcessingFaculty of Informatics
- 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).
- prof. PhDr. Karel Pala, CSc. (lecturer), doc. RNDr. Aleš Horák, Ph.D. (deputy)
RNDr. Vojtěch Kovář, Ph.D. (assistant)
RNDr. Zuzana Nevěřilová, Ph.D. (alternate examiner)
- Guaranteed by
- prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing - Faculty of Informatics
Contact Person: prof. PhDr. Karel Pala, CSc.
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
- Wed 12:00–13:50 C416
- 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 the course is directly associated with
- there are 55 fields of study the course is directly associated with, display
- Course objectives
- The course offers a deeper knowledge about the natural language processing and computational linguistics.
- Learning outcomes
- The students will learn about the particular levels of linguistic analysis - morphology, syntax, semantics and pragmatics.
They will be able to use language data - corpora, types of corpora, corpus tools, perform tagging corpus texts, disambiguation with rule based and statistical systems.
They will be acquainted with representation of the morphological stuctures, notation and algorithms for morphological analysis.
The students will be able to work with the representations of syntactic structures - formal grammars and their types. They will learn about context-free, functional and definite-clause grammars and related parsing algorithms.
The data structures such as valency frames and their types will be explained as well.
They will learn about lexical semantics - meanings of words and collocations, machine readable dictionaries, lexical databases (WordNet, EuroWordNet, thesauri).
Semantic analysis of sentence, principles of logical semantic and Normal Translation Algorithm will be presented.
Pragmatics and discourse analysis and its segmentation, anaphora and (co-)reference will be explained.
The students obtain basic knowledge about dialogue systems, inference systems and knowledge representation for NLP systems.
They will be able to understand the principles of the communication agents and main evaluation techniques.
- Natural language processing and computational linguistics.
- Natural language and understanding.
- Levels of linguistic analysis - morphology, syntax, semantics.
- Language data - corpora. Types of corpora. Corpus tools. Tagging corpus texts. Disambiguation, rule based and statistical systems.
- Representation of the morphological stuctures, notation, morphological algorithms.
- Representation of syntactic structures - formal grammars and their types. Context-free and definite-clause grammars. Parsing algorithms. Valency frames and their types.
- Semantic representation. Lexical meanings (words and collocations), machine readable dictionaries, lexical databases (WordNet, EuroWordNet, thesauri).
- Semantic analysis of sentence meaning, Normal Translation Algorithm.
- Discourse analysis and its segmentation. Anaphora and (co-)reference.
- Inference and knowledge representation for NL systems.
- Dialogue systems.
- Communication agents.
- Evaluation techniques
- Teaching methods
- Teaching is performed in the form of oral lectures and seminars, in which the slides and demos of the relevant software tools are combined. Students work out homeworks, prepare presentations based on the literature they had read and develop smaller projects. At the appropriate points of the teaching the open dialog between a teacher and students is used.
- Assessment methods
- written test
- Language of instruction
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/autumn2019/PA153