Acquisition of knowledge in the field of Computational Linguistics allowing students:
a) to develop basic skills for the analysis and understanding of natural languages and of natural languages functioning aiming at computation;
b) to understand the levels of computer-aided treatment involved in the processing and computation of natural languages;
c) to know the different current strategies and tools for the processing and computation of natural languages;
d) to acquire methodologies for the computer-aided analysis and treatment of linguistic data;
e) to make use, in practical scenarios, of the acquired knowledge.
Raquel Fonseca Amaro
Weekly - 4
Total - 168
Bolshakov, I. & Gelbukh, A. (2004) Computational Linguistics: Models, Resources, Applications, IPN, UNAM, FCE.
Jargas, A. M. (2012) Expressões Regulares - uma abordagem divertida. 4ª edição. Novatec.
Mitrov, R. (2003) The Oxford Handbook of Computational Linguistics. Oxford University Press.
Nuges, P. M. (2014) Language Processing with Perl and Prolog. Springer.
Tsujii, J. (2011) Computational Linguistics and Natural Language Processing. In: Gelbukh A.F. (Eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer. https://doi.org/10.1007/978-3-642-19400-9_5
Vieira, R. & Lima, V. L. S. (2001) Lingüística computacional: princípios e aplicações. In: A. T. Martins & D. L. Borges. (Orgs.). As Tecnologias da informação e a questão social: anais. 1 ed. SBC, v. 3, p. 47-88.
Theoretical and practical classes and tutorial guidance, with resource to case studies and practical application of the acquired knowledge, including: i) topic presentation and explanation by the teacher; ii) discussion and analytic analysis of relevant literature on the addressed topics; iii) practical application of acquired knowledge in individual and collective essays within specific tasks, including programming tasks and formal knowledge modelization tasks.
Evaluation Methodologies - Continuous evaluation, including the following components: individual tests and individual and collective essays, presented in class (specific weight of each element to be defined with the students).(100%)
1. Linguistics, computation and natural language processing (NLP)
1.1 Introduction to Computational Linguistics
1.2 Computational Linguistics issues and subfields of Linguistics
2. Processing and computation of natural languages
2.1 Regular expressions
3. Computational Linguistics historic perspective and strategies
3.1 Fields and models from Linguistics
3.2 Applications and tools
3.3 Machine Learning
4. The role of Linguistics in the development of NLP applications
4.1 Data annotation
4.2 Quality evaluation
4.3 Hybrid models of computation
Programs where the course is taught: