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Fundamentals of Natural Language

Code: 106584
Credits: 6
2026/2027
Degree programme Type Course
Bachelor in Artificial Intelligence OB 2

Contact lecturer

Name :
Ernest Valveny Llobet
Email :
ernest.valveny@uab.cat

Teaching staff

Pau Torras Coloma

Group languages

You can consult this information at the end of the document.

Prerequisites

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 and Fundamentals of Machine Learning.

Objectives

This course provides an overview of the fundamentals techniques for natural language processing (NLP), covering classical approaches for text processing and parsing, language and sequence modelling and text representation, showing their application to usual NLP problems. The course also covers an introduction to the application of deep learning techniques to NLP and to the foundations of modern LLMs

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.

Learning outcomes

  • CM14 (Design solutions to natural language processing projects in any field and application.) Design solutions to natural language processing projects in any field and application.
  • KM31 (Explain the linguistic concepts required to develop automatic natural language processing applications.) Explain the linguistic concepts required to develop automatic natural language processing applications.
  • KM32 (Describe the machine learning models, architectures, training procedures, techniques, and methods used in the development of natural language processing systems.) Describe the machine learning models, architectures, training procedures, techniques, and methods used in the development of natural language processing systems.
  • SM34 (Use text processing, language modelling, text representation, and sequence analysis techniques and algorithms for the implementation of natural language processing solutions.) Use text processing, language modelling, text representation, and sequence analysis techniques and algorithms for the implementation of natural language processing solutions.
  • SM35 (Apply existing text generation and rendering models to different types of language processing problems.) Apply existing text generation and rendering models to different types of language processing problems.
  • SM37 (Apply methodologies for evaluating and analysing natural language processing systems that critically consider both performance and potential ethical, legal, and societal biases and implications.) Apply methodologies for evaluating and analysing natural language processing systems that critically consider both performance and potential ethical, legal, and societal biases and implications.

Contents

  1. Introduction to linguistics and NLP
  2. Basic text processing
  3. Syntactic parsing
  4. Language modelling
  5. Semantic text representation
  6. Sequence labelling
  7. Deep learning for language processing
  8. Foundations of LLMs

Learning activities and methodology

Title Hours ECTS Learning outcomes
Theory classes 25 1
Problems sessions 16 0.64
Work on the project 50 2
Individual studying 24 0.96
Project sessions 4 0.16
Problem solving (individual) 25 1

There will be three types of teaching activities: theory classes, solving practical exercises individually (problems) and developing a project in small groups of 2-3 students.

  1. Theory classes: Presentation of the theoretical content of the subject. For each of the topics studied, the main theoretical concepts and mathematical formulation are exposed, as well as the corresponding algorithmic solutions.
  2. Laboratory sessions: The laboratory sessions aim to facilitate interaction and reinforce the understanding of the topics covered in the theory classes. During the laboratory sessions, we will address two types of activities: the resolution of practical exercises (problems) and the monitoring and presentation of projects.

2.1. Problems: A set of problems to work through will be used, provided in Jupyter notebooks that exemplifies the coding details of the concepts exposed during theory classes. Work on the problems will begin in class and must be completed at home. Students will be required to make regular submissions of their work, which will comprise the problems portfolio. In some class sessions, some evaluation activities about the delivered exercises may be carried out.

2.2. Project: A project will be carried out during the semester, where students will have to solve a specific problem of certain complexity. The project will be solved in small groups of 2-3 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).


Annotation: 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.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Project 40 2 0.08 CM14, SM34, SM35, SM37
Exams 40 4 0.16 KM31, KM32
Problem solving 20 0 0 SM34, SM35, SM37

The evaluation will be continuous, so there is no unique evaluation modality. 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 = 0.4 * Theory Grade + 0.2 * Problems Grade + 0.4 * Project Grade

This formula will be applied as long as the theory and the Project grades, are higher than 5. There is no restriction on the problems grade. If doing the calculation of 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 given.

Theory Grade

The theory grade aims to assess the individual abilities of the student in terms of the theoretical content of the subject. This is done continuously during the course through two partial exams:

Theory Grade = 0.5 * Grade Exam 1 + 0.5 * Grade Exam 2

The mid-term exam (Exam 1) is done in the middle of the semester and serves to eliminate part of the subject if it is passed. The final exam (Exam 2) is done at the end of the semester and serves to eliminate the rest of subject if it is passed.

In order to obtain a final pass theory grade, it will be required for the partial exam grades 1 and 2 to be both higher than 4.5 and the average to be above 5.0.

Recovery exam: In case the theory grade does not reach the adequate level to pass, the students can take a recovery exam, destined to recover the failed part (1, 2 or both) of the continuous evaluation process.

Problems Grade

The aim of the problems is for the student to become familiar with the practical implementation of the theoretical concepts. 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 problems grade will be 0.

There will be an individual assessment of the student's work on the problems which may consist of exam questions about the problems or some other type of evaluation activities during class sessions. The final grade for the problems will be the combination of the problem dossier and the individual assessment.

Problems Grade = 0.5 * Portfolio evaluation + 0.5 * Individual Assessment

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 the defined challenge. In addition, the students must demonstrate their teamwork skills and present the results to the class.

The project is evaluated through its deliverable, an oral presentation that students will make in class, and an individual-evaluation process. The participation of students in all three activities (preparing the deliverable, presentation and individual evaluation) is necessary in order to obtain a projects grade. The grade is calculated as follows:

Project Grade = 0.6 * Grade Deliverables + 0.2 * Grade Presentation + 0.2 * Grade Individual evaluation

If performing the above calculation yields >= 5 but the student did not participate in any of the activities (deliverable, presentation, individual evaluation), then a final grade of 4.5 will be given to the corresponding project.

In case the deliverable is presented, but the final project grade does not reach the minimum of 5, there will be a recovery of the project. In case of not presenting the deliverable or considering it copied, there will be no recovery and the subject will be considered failed. The maximum project grade that can be obtained in case of recovery is 7.

Students repeating the year can have their project grade from the previous year validated. In this case, regardless of the grade obtained in the previous year, the project grade will be a 5.


Important notes

Notwithstanding other disciplinary measures deemed appropriate, and in accordance with the academic regulations in force, evaluation activities will be suspended with zero (0) whenever a student commits any academic irregularities that may alter such evaluation (for example, plagiarizing, copying, letting copy, ...). The evaluation activities qualified in this way and by this procedure will not be recoverable. If you need to pass any of these assessment activities to pass the subject, this subject will be failed directly, without opportunity to recover it in the same year.

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.

In case the student does not deliver any problems solutions, does not attend any project presentation session during the laboratory sessions and does not take any exam, the corresponding grade will be a \"non-evaluable\". In another case, the \"no shows\" count as a 0 for the calculation of the weighted average.

In order to pass the course with honours, the final grade obtained must be equal or higher than 9 points. Because the number of students with this distinction cannot exceed 5% of the total number of students enrolled in the course, it is given to whoever has the highest final marks. In case of a tie, the results of the partial exams will be taken into account.

Bibliography

  • D. Jurafsky, JH Martín. Procesamiento del habla y el lenguaje . Tercera edicion. 2021 < https://web.stanford.edu/~jurafsky/slp3/ >
  • J. Eisenstein. Procesamiento del lenguaje natural . 2018. Prensa del MIT
  • H. Lane, C. Howard, HM Hapke. Procesamiento del lenguaje natural en acción . 2019. Publicaciones de Manning
  • Kenny, Dorothy, ed. Traducción automática para todos . Prensa científica lingüística, 2022. < https://langsci-press.org/catalog/book/342 > _ _
  • Rowe, Bruce M. y Diane P. Levine. Una breve introducción a la lingüística . 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.

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 71 English second semester afternoon
(PAUL) Classroom practices 711 English second semester afternoon
(PLAB) Practical laboratories 711 English second semester morning-mixed
(PLAB) Practical laboratories 712 English second semester morning-mixed