Competencies and objectives

 

Course context for academic year 2024-25

The course Técnicas de Procesamiento de Lenguaje Natural presents the fundamentals of the computational approach to human language. In this course, it is assumed that students have successfully completed the course Técnicas de Aprendizaje Automático and have acquired the knowledge and skills covered there.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25

Transversal Competences

  • CT1 : Be able to lead projects related to artificial intelligence, as well as to manage work teams.
  • CT2 : Demonstrate computer and information skills in the field of artificial intelligence.
  • CT3 : Show oral and written communication skills.

 

General Competences

  • CG1 : Apply the knowledge acquired to real problems related to artificial intelligence.
  • CG10 : Be able to use engineering principles and modern computer technologies to research, design and implement new applications of artificial intelligence,
  • CG2 : Be able to develop and learn in a self-directed or autonomous way topics related to artificial intelligence.
  • CG3 : Know how to operate in multidisciplinary and/or international contexts, providing solutions from the point of view of artificial intelligence.
  • CG5 : Know how to manage the available information and resources related to artificial intelligence.

 

Specific Competences

  • CE10 : Identify appropriate techniques for different natural language processing problems.
  • CE11 : Develop and evaluate methods related to natural language processing.

 

Basic Competences

  • CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
  • CB6 : Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
  • CB7 : That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ¿¿study
  • CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
  • CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.

 

 

 

Learning outcomes (Training objectives)

- Link the theoretical foundations of computational linguistics with the development of specific natural language processing techniques.

- Analyse texts written in natural language at the lexical, syntactic, semantic and pragmatic levels.

- Contrasting symbolic, statistical and neural approaches in natural language processing.

- Know the particularities of machine learning in the context of natural language processing.

- Determine the appropriate machine learning models to solve a natural language processing problem with special emphasis on those that provide good results according to the current state of the art.

- Know the fundamentals of computational models of speech processing at the phonetic, recognition and synthesis levels.

- Know the basics of the most widely used tools and libraries in natural language processing.

- Know how to download the appropriate data and corpus to train, adapt and evaluate machine learning models in the context of language processing.

- Take advantage of the benefits of pre-trained, multilingual and multimodal models.

 

 

Specific objectives stated by the academic staff for academic year 2024-25

  • Link the theoretical foundations of computational linguistics with the development of specific techniques for natural language processing.
  • Analyze written texts in natural language computationally at the lexical, syntactic, semantic, and pragmatic levels.
  • Contrast symbolic, statistical, and neural approaches in natural language processing.
  • Understand the particularities of machine learning in the context of natural language processing, with special emphasis on the methods that provide the best results according to the current state of the art.
  • Understand the fundamentals of computational models for speech processing at the phonetic, recognition, and synthesis levels.
  • Download the appropriate data and corpora to train, adapt, and evaluate machine learning models in the context of natural language processing.
  • Take advantage of pretrained, multilingual, and multimodal models.

 

 

General

Code: 43505
Lecturer responsible:
Pérez Ortiz, Juan Antonio
Credits ECTS: 4,50
Theoretical credits: 0,90
Practical credits: 0,90
Distance-base hours: 2,70

Departments involved

  • Dept: LANGUAGES AND COMPUTING SYSTEMS
    Area: LANGUAGES AND COMPUTING SYSTEMS
    Theoretical credits: 0,9
    Practical credits: 0,9
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught