nireEHU 
Subject
LCT at the University of Malta (Malta)
General details of the subject
- Mode
- Face-to-face degree course
- Language
- English
Description and contextualization of the subject
El estudiante realiza un primer curso completo en la misma.Competencies
| Name | Weight |
|---|---|
| Conocimiento de las tecnologías del lenguaje basado en conocimiento lingüístico y estadístico. | 100.0 % |
Study types
| Type | Face-to-face hours | Non face-to-face hours | Total hours |
|---|---|---|---|
| Lecture-based | 200 | 300 | 500 |
| Applied computer-based groups | 400 | 600 | 1000 |
Training activities
| Name | Hours | Percentage of classroom teaching |
|---|---|---|
| Analysing and discussing papers | 250.0 | 80 % |
| Computer work practice, laboratory, site visits, field trips, external visits | 500.0 | 60 % |
| Lectures | 500.0 | 40 % |
| Seminars | 250.0 | 80 % |
Assessment systems
| Name | Minimum weighting | Maximum weighting |
|---|---|---|
| Drawing up reports and presentations | 25.0 % | 25.0 % |
| Practical tasks | 25.0 % | 25.0 % |
| Written examination | 50.0 % | 50.0 % |
Learning outcomes of the subject
* Tener conocimientos básicos de teorías sintácticas y semánticas* Ser capaz de evaluar la importancia de las teorías sintácticas y semánticas para el PLN
* Ser capaz de procesar, visualizar e interpretar datos del lenguaje cuantitativos extraídos del corpus a través de interfaces web
* Ser capaz de evaluar la importancia de los datos extraídos del corpus para estudios teóricos y de lingüística aplicada
* Tener conocimiento de los formalismos y estándares de anotación usados para analizar y anotar texto a nivel léxico, sintáctico, semántico y a nivel del discurso
* Ser capaz de aplicar estos estándares a nuevos datos, de evaluar los resultados y de usar y desarrollar métodos para su anotación automática
Temary
1. Foundational: Statistical methods; symbolic methods; cognition; corpus; text and speech; foundations of linguistics2. Computational Syntax and Morphology: Finite state techniques; probabilistic approaches; formal grammars; tagging, chunking; parsing.
3. Computational Semantics, Pragmatics and Discourse. Syntax-semantics interface; semantic construction; dialogue; ontologies; formal semantics.
4. Data Structures, Data Organization and Processing: Algebraic data-types; relational databases; semi-structured data and XML; information retrieval; digital libraries.
5. Logic, Computability and Complexity: Logic and inference; automata theory; computability theory; complexity theory; discrete mathematics
6. Formal Languages and Algorithms: Formal grammars and languages hierarchy; parsing and compiler design; search techniques and constraint resolution; automated learning.
7. 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.
8. Advance Computer Science: Artificial intelligence; knowledge representation; automated reasoning; semantic web; intelligent and multi-modal interfaces; cognitive modelling; computational psychology; neural networks; machine learning.