| Código |
16684
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| Ano |
3
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| Semestre |
S1
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| Créditos ECTS |
6
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| Carga Horária |
PL(30H)/T(30H)
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| Área Científica |
Informática
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Learning outcomes |
Natural Language Processing (NLP) seeks to computationally understand and reproduce language, a core aspect of human intelligence. This course combines foundations in linguistics, statistics, logic, and machine learning, covering both classical techniques and recent models such as transformers. Beyond methods, NLP enables everyday applications: search engines, virtual assistants, machine translation, sentiment analysis, and data science support. Mastery of these tools develops technical skills and a critical perspective on the limits, challenges, and responsibilities of systems handling human language. The learning path is practice-oriented: working with real corpora, experimenting with pre-trained models and specialized libraries, and building applicable solutions. By the end, students are expected not only to apply existing tools but also to evaluate and design creative, responsible approaches to new challenges involving text and natural language.
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Syllabus |
1. Introduction, context, and motivation
2. Basic textual operations
3. Lexical modeling
4. Syntactic modeling
5. Document vectorization
6. Lexical vectorization
7. Semantic representation
8. Neural models in NLP
9. Computing with human language
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Main Bibliography |
[1] Daniel Jurafsky and James H. Martin. 2025. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3rd edition. Online manuscript released August 24, 2025. https://web.stanford.edu/~jurafsky/slp3.
[2] Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python (1st. ed.). O'Reilly Media, Inc.
[3] Tunstall, L., Von Werra, L., & Wolf, T. 2022. Natural language processing with transformers. " O'Reilly Media, Inc.".
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| Language |
Portuguese. Tutorial support is available in English.
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