Competencies and objectives

 

Course context for academic year 2024-25

The subject Applications of Natural Language Processing focuses on the use of natural language processing techniques to support tasks involving language comprehension, natural language text generation and speech processing. This subject is a continuation of the subject Natural Language Processing Techniques; therefore, it is assumed that students have satisfactorily completed this subject and that they have acquired the knowledge and competences that are specific to it.

 

 

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.
  • CG4 : Know and apply in each situation the social, ethical and legal responsibilities linked to artificial intelligence.
  • CG6 : Being able to adapt to environments related to artificial intelligence, fostering teamwork, creativity,
  • CG7 : To be able to adapt to the constant evolution of the discipline and to understand and apply new technical and scientific developments related to artificial intelligence.
  • CG8 : Knowing how to plan, design, develop, implement and maintain products, applications and services related to artificial intelligence, taking into account technical, economic and efficiency aspects.
  • CG9 : Know how to manage projects related to artificial intelligence, complying with current regulations and ensuring the quality of the service.

 

Specific Competences

  • CE13 : Extrapolate the basic techniques of natural language processing for specific applications to other problems that can be solved using these techniques.
  • CE14 : Detect real technological contexts in which natural language processing can provide useful solutions.
  • CE15 : Extract the characteristics common to the different applications of natural language processing while differentiating their particularities. differentiate their particularities.

 

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)

- Apply, develop, train, implement and evaluate the appropriate techniques to solve a real-world problem involving the use of natural language.

- Know the main current applications of language understanding based on the state of the art, such as information retrieval applications, answer search, document classification, sentiment analysis, fact checking and hoax detection, etc.

- Know the main current applications of natural language generation based on the state of the art, such as text autocompletion applications, automatic summaries, dialogue systems, etc.

- To know the techniques and models currently used in machine translation and their adaptation to other sequence translation tasks.

- To understand the principles of speech recognition in current models and their integration, for example, in conversational systems.

- Optimise the performance of the natural language processing applications implemented to obtain efficient systems that can be used in production.

- Identify the different types of biases present in natural language processing systems as well as their prevention and treatment.

 

 

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

  • To know the main applications of language comprehension and know how to choose, train and evaluate machine learning models for tasks such as information retrieval, question answering, document classification or sentiment analysis, among others.
  • To know the main applications of natural language generation and know how to choose, train and evaluate machine learning models for tasks such as the generation of abstract summaries, generation of reports and/or news, or dialogue systems, among others.
  • To know the main applications of machine translation and know how to choose, train and evaluate machine learning models for translation in specific contexts.
  • To know the main applications of speech processing and know how to choose, train and evaluate machine learning models for speech processing.
  • To identify the different types of biases present in natural language processing systems as well as their prevention and treatment.

 

 

General

Code: 43508
Lecturer responsible:
Sánchez Martínez, Felipe
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