XSL Content

LCT at the Charles University in Prague (Czech Republic)

General details of the subject

Face-to-face degree course

Description and contextualization of the subject

El estudiante realiza las asignaturas del 2º curso en la misma.


Conocimiento de las tecnologías del lenguaje sobre la base de los algoritmos de aprendizaje automático y aprendizaje profundo.100.0 %

Study types

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Applied computer-based groups200300500

Training activities

NameHoursPercentage of classroom teaching
Analysing and discussing papers125.080 %
Computer work practice, laboratory, site visits, field trips, external visits250.060 %
Lectures250.040 %
Seminars125.080 %

Assessment systems

NameMinimum weightingMaximum weighting
Drawing up reports and presentations25.0 % 25.0 %
Practical tasks25.0 % 25.0 %
Written examination50.0 % 50.0 %

Learning outcomes of the subject

* Tener conocimiento y manejo de algoritmos de aprendizaje automático

* Tener criterios para diseñar experimentos para procesamiento del lenguaje

* Tener conocimiento sobre estadística inferencial con el foco en los métodos usados habitualmente en el área de la lingüística experimental

* Tener conocimientos de las técnicas principales de traducción automática en PLN, especialmente de las técnicas basadas en aprendizaje profundo.


* Foundational : Statistical methods; symbolic methods; cognition; corpus; text and speech; foundations of linguistics.

* Computational Syntax and Morphology : Finite state techniques; probabilistic approaches; formal grammars; tagging, chunking; parsing.

* Computational Semantics, Pragmatics and Discourse: Syntax-semantics interface; semantic construction; dialogue; ontologies; formal semantics.

* Data Structures, Data Organization and Processing : Algebraic data-types; relational databases; semi-structured data and XML; information retrieval; digital libraries.

* Logic, Computability and Complexity: Logic and inference; automata theory; computability theory; complexity theory; discrete mathematics.

* Formal Languages and Algorithms: Formal grammars and languages hierarchy; parsing and compiler design; search techniques and constraint resolution; automated learning.

* Advanced Language Technologies: Machine translation; information and knowledge representation; information retrieval; question answering; speech recognition and generation; models of human language processing and understanding; psycholinguistics.

* Advanced Computer Science: Artificial intelligence; knowledge representation; automated reasoning; semantic web; intelligent and multi-modal interfaces; cognitive modelling; computational psychology; neural networks; machine learning.