Aprendizaje y Procesamiento del Lenguaje Natural
Código: 106585Créditos: 6
| Titulación | Tipo | Curso |
|---|---|---|
| Inteligencia Artificial / Bachelor in Artificial Intelligence | OP | 3 |
Profesor/a de contacto
- Nombre :
- Xim Cerdà Company
- Correo electrónico :
- joaquin.cerda@uab.cat
Equipo docente
- Ernest Valveny Llobet
Idiomas de los grupos
Puede consultar esta información al final del documento.
Prerrequisitos
There are no official prerequisites, but it is recommended to have completed the subjects of Fundamentals of Programming I and II, Fundamentals of Mathematics I and II, Probability and Statistics, Data Engineering, Fundamentals of Machine Learning, and Fundamentals of Natural Language.
Objetivos
This course provides an overview of the Natural Language Processing (NLP) applications, from classical approaches for text processing to advanced methods for person-computer interaction. This course covers both machine learning and deep learning techniques for NLP, considering both text and speech processing.
By the end of this course, students will be able to:
- Understand the fundamental concepts and techniques used in NLP.
- Implement and evaluate various NLP techniques using Python and popular NLP libraries.
- Apply NLP methods to real-world problems and interpret the results.
Resultados de aprendizaje
- CM14 (Diseñar soluciones a proyectos de procesamiento del lenguaje natural en cualquier ámbito y aplicación) Diseñar soluciones a proyectos de procesamiento del lenguaje natural en cualquier ámbito y aplicación
- CM15 (Evaluar la adecuación de sistemas de generación de lenguaje a una determinada aplicación, en función de su posible impacto en aspectos éticos, legales y sociales como pueden ser el sesgo, la discriminación, la veracidad o la privacidad y la seguridad de los datos.) Evaluar la adecuación de sistemas de generación de lenguaje a una determinada aplicación, en función de su posible impacto en aspectos éticos, legales y sociales como pueden ser el sesgo, la discriminación, la veracidad o la privacidad y la seguridad de los datos.
- KM32 (Describir los modelos, arquitecturas, procedimientos de entrenamiento, técnicas y métodos de aprendizaje automático que se utilizan en el desarrollo de sistemas de procesamiento de lenguaje natural) Describir los modelos, arquitecturas, procedimientos de entrenamiento, técnicas y métodos de aprendizaje automático que se utilizan en el desarrollo de sistemas de procesamiento de lenguaje natural
- SM34 (Utilizar técnicas y algoritmos de procesado de texto, modelado del lenguaje, representación de texto y análisis de secuencias para la implementación de soluciones de procesamiento de lenguaje natural) Utilizar técnicas y algoritmos de procesado de texto, modelado del lenguaje, representación de texto y análisis de secuencias para la implementación de soluciones de procesamiento de lenguaje natural
- SM35 (Aplicar modelos de representación y generación de texto existentes a diferentes tipos de problemas de procesamiento del lenguaje) Aplicar modelos de representación y generación de texto existentes a diferentes tipos de problemas de procesamiento del lenguaje
- SM36 (Programar un uso eficiente y óptimo de las infraestructuras y recursos necesarios para el entrenamiento y aplicación de modelos de procesamiento de lenguaje natural) Programar un uso eficiente y óptimo de las infraestructuras y recursos necesarios para el entrenamiento y aplicación de modelos de procesamiento de lenguaje natural
- SM37 (Aplicar metodologías de evaluación y análisis de sistemas de procesamiento del lenguaje natural que tengan en cuenta de forma crítica tanto el rendimiento como los posibles sesgos e implicaciones éticas, legales y sociales) Aplicar metodologías de evaluación y análisis de sistemas de procesamiento del lenguaje natural que tengan en cuenta de forma crítica tanto el rendimiento como los posibles sesgos e implicaciones éticas, legales y sociales
Contenidos
- Fundamentos de NLP
- Análisis semántico y pragmático
- Transformers para aplicaciones de NLP
- Fundamentos de los LLMs
- Técnicas de mejora de los LLMs
- Extensiones de los LLMs
- Modelos para el reconocimiento de voz y audio
Actividades formativas y Metodología
| Título | Horas | ECTS | Resultados de aprendizaje |
|---|---|---|---|
| Clases de teoría | 15 | 0,6 | |
| Trabajo en el proyecto | 50 | 2 | |
| Estudio individual | 25 | 1 | |
| Sesión de proyecto | 6 | 0,24 | |
| Resolución de ejercicios | 25 | 1 | |
| Sesión de ejercicios | 25 | 1 |
Sessions will combine three types of teaching activities: theory classes, solving project-based exercises, and a project development. Students will work individually to solve the exercises and in small groups of two or three people for the project.
1. Theory lectures, which will contain the theoretical background needed to solve the exercises and the project will be presented using a presentation. These presentations will contain theoretical concepts, and mathematical formulation, as well as the corresponding algorithmic solutions.
2. Project-based exercises will be developed using Jupyter notebooks to be able to comment, point by point, the coding solutions. These exercises will be submitted regularly through Campus Virtual, explaining the proposed solution and showing the obtained results. Optionally, these exercises could include a report or a presentation for its evaluation. The exercise’s submissions will comprise the portfolio.
3. A project will be carried out during the semester, where students will have to solve a real-world problem. The project will be solved in small groups of two or three students, where each member of the group must contribute a part and put it together with the rest to obtain the final solution. These working groups must be maintained until the end of the semester and must be self-managed in terms of distribution of roles, work planning, assignment of tasks, management of available resources, conflicts, etc. To develop the project, the groups will work autonomously, while the practical sessions will be used (1) for the teacher to present the project theme and discuss possible approaches, (2) for monitoring the status of the project and (3) for the teams to present their final results.
The above activities will be complemented by a system of tutoring and consultations outside class hours.
All the information of the subject and the related documents that the students need will be available at the virtual campus (cv.uab.cat).
Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Note: Fifteen minutes of one class session, within the schedule established by the school/degree programme, will be reserved for students to complete the surveys evaluating both the teaching performance of the instructor and the course/module
Evaluación
Actividades de evaluación continuada
| Título | Peso | Horas | ECTS | Resultados de aprendizaje |
|---|---|---|---|---|
| Proyecto | 30% | 2 | 0,08 | CM14, CM15, KM32, SM34, SM35, SM36, SM37 |
| Portfolio | 49% | 0 | 0 | CM14, CM15, KM32, SM34, SM35, SM36, SM37 |
| Test | 21% | 2 | 0,08 | CM14, CM15, KM32, SM34, SM35, SM36, SM37 |
To assess the level of student learning, a formula is established that combines knowledge acquisition, the ability to solve problems and the ability to work as a team, as well as the presentation of the results obtained.
Final grade
The final grade is calculated in the following way and according to the different activities that are carried out:
Final grade = 0.7 * Exercises Grade + 0.3 * Project Grade
This formula will be applied as long as the Exercises and Project grades, are higher than 4. If the formula yields >= 5, but the student does not reach the minimum required in any of the evaluation activities, then a final grade of 4.5 will be assigned.
Exercises Grade
The aim of the exercises is to become familiar with the use of the theoretical concepts, and to apply them to a real-world problem. The regular submission of problem solutions will be used as evidence of this work.
In order to obtain a grade for exercises, it is necessary that more than 50% of the exercises are submitted during the semester. In the contrary, the portfolio grade will be 0.
A test about the exercises will be performed individually at the end of the semester. The final problems grade will be the combination of the exercise’s portfolio and this test.
Problems Grade = 0.7 * Portfolio evaluation + 0.3 * Test
The formula will be applied as long as the Test grade is higher than 4.
At the end of the semester, students will have the opportunity to re-submit two different deliveries for retaking, and they will also be able to retake the Test. After the retakes, the maximum grade which can be obtained is 8.
Project Grade
The project carries an essential weight in the overall mark of the subject. Developing the project requires that the students work in groups and design an integral solution to thedefined challenge. In addition, the students must demonstrate their teamwork skills and present the results to the class.
The project is evaluated through its report, an oral presentation that students will present in class, and an individual-evaluation process. The participation of students in all three activities (preparing the report, presentation and individual evaluation) is necessary in order to obtain a projects grade. The grade is calculated as follows:
Project Grade = 0.6 * Report + 0.2 * Presentation + 0.2 * Individual evaluation
If performing the above calculation yields >= 5 but the student did not participate in any of the activities (report, presentation, individual evaluation), then a final grade of 4.5 will be given to the corresponding project.
There will be a retake of the project in case the final project grade does not reach the minimum of 4. In case of copy, there will be no recovery and the subject will be considered failed. The maximum project grade that can be obtained in case of retake is 7.
In this subject, the use of generative Artificial Intelligence technologies is allowed in a controlled manner. The student must clearly identify which parts of his/her work have been carried out with the support of generative AI tools and, in any case, must be able to understand, explain and justify the work carried out. The lack of transparency in the use of generative AI will be considered a lack of academic honesty and may lead to a partial or total penalty in the grade of the activity.
Bibliografía
- D. Jurafsky, J.H. Martin. Speech and Language Processing. Third Edition. 2021 <https://web.stanford.edu/~jurafsky/slp3/>
- R.S.T. Lee. Natural Language Processing. 2024. Springer
- G. Paab. Foundation Models for Natural Language Processing. 2023. Springer
- J. Eisenstein. Natural Language Processing. 2018. MIT Press
- H. Lane, C. Howard, H. M. Hapke. Natural Language Processing in Action. 2019. Manning Publications
- Kenny, Dorothy, ed. Machine translation for everyone. Lanugage Science Press, 2022. < https://langsci-press.org/catalog/book/342>
- Rowe, Bruce M., and Diane P. Levine. A concise introduction to linguistics. Routledge, 2018.
Software
For the problems and projects of the course we will use Python, along with some Python libraries for NLP that will be specified during the course.
Grupos e idiomas de la asignatura
La información proporcionada es provisional hasta el 30 de noviembre. A partir de esta fecha, podrá consultar el idioma de cada grupo a través de este enlace. Para acceder a la información, será necesario introducir el CÓDIGO de la asignatura
| Tipo de docencia | Grupo | Idioma | Semestre | Turno |
|---|---|---|---|---|
| (TE) Teoría | 71 | Inglés | segundo cuatrimestre | tarde |
| (PAUL) Prácticas de aula | 711 | Inglés | segundo cuatrimestre | tarde |
| (PLAB) Prácticas de laboratorio | 711 | Inglés | segundo cuatrimestre | tarde |