| Code |
14507
|
| Year |
1
|
| Semester |
S1
|
| ECTS Credits |
6
|
| Workload |
OT(15H)
|
| Scientific area |
Informatics
|
|
Entry requirements |
Have maturity in the field of computer programming in order to be able to implement the proposed work. Knowledge of Artificial Intelligence and Machine Learning are an asset.
|
|
Learning outcomes |
To know the most relevant foundations in the field of Computational Linguistics (CL), in terms of its aspects: morphological, syntactic, semantic and pragmatic, with special emphasis on the semantic and pragmatic levels. Know the main approaches, techniques, and resources associated with CL. Skills: Have the ability to identify the relevance of CL in general problems and use it for improvement. For example, improving the human-machine interaction of a system through the use of CL. Competences: At the end, the student should have the ability to take an open CL problem, design and implement an experimental plan that combines knowledge, resources, and data, aiming for new results and subsequent scientific dissemination. The student should also be able to apply the concepts of CL, as well as their most relevant techniques and resources, to any scientific or industrial problem, aiming their resolution or improvement.
|
|
Syllabus |
Module A — Fundamentals and Approaches
1. Introduction and Fundamentals;
2. The Five Levels of Analysis in PLN: Morphological, Lexical, Syntactic, Semantic, Pragmatic;
3. Probabilistic Language Modeling;
4. Logic, Ontologies, and Discourse;
5. Large Language Models (LLMs).
Module B — Resources, Technologies and Applications
— Information Retrieval and Extraction;
— Text Classification and Clustering;
— Automatic Summarization;
— Automatic Translation;
— Sentiment Analysis.
|
|
Main Bibliography |
Jurafsky, D., Martin, J. (2008/2023?). SPEECH and LANGUAGE PROCESSING: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Second/Third Edition. McGraw Hill. ISBN: 978-0131873216.
Manning, D., Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press. ISBN: 978-0262133609.
Bird, S., Klein, E., Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly Media. ISBN: 978-0596516499.
Reese, R. (2015). Natural Language Processing with Java (Community Experience Distilled). Packt Publishing. ISBN: 978-1784391799.
Sowa, J. (2000) Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole, 2000. ISBN: 9780534949655
Vieira, R., & Lima, V. L. (2001). Lingüística computacional: princípios e aplicações. In Anais do XXI Congresso da SBC. I Jornada de Atualização em Inteligência Artificial (Vol. 3, pp. 47-86). sn.
|
|
Teaching Methodologies and Assessment Criteria |
Throughout the semester, students are expected to engage in a continuous stream of work, with guidance and supervision from the instructor. Additionally, students are anticipated to complete a literature review assignment related to Computational Linguistics and to carry out experimental work, preferably within the same field. A desirable objective for this course is the production of a scientific article.
|
|
Language |
Portuguese. Tutorial support is available in English.
|