Computational Linguistics
Objectives
Acquisition of knowledge in the field of Computational Linguistics allowing students:
a) to develop skills for the analysis and understanding of natural languages aiming at computation;
b) to understand the levels of treatment and knowledge modelling, particualrly at lthe lexical level, used in the processing of natural languages;
c) to know current strategies and tools for the processing and computation of natural languages;
d) to acquire methods and practice of linguistic data analysis envisaging the modelling of linguistic knowledge for computation purposes;
General characterization
Code
722131042
Credits
10.0
Responsible teacher
Raquel Fonseca Amaro
Hours
Weekly - 3
Total - 280
Teaching language
Portuguese
Prerequisites
None
Bibliography
Amaro, R. & S.Mendes (2016) Lexicologia e Linguística Computacional. In: Martins, A. M. & E. Carrilho (eds.) Manual de Linguística Portuguesa, Manuals of Romance Linguistics, Mouton de Gruyter, pp. 178-199.
Bolshakov, I. & Gelbukh, A. (2004) Computational Linguistics: Models, Resources, Applications. IPN, UNAM, FCE.
Hovy, E. & Lavid, J. (2010)Towards a 'Science' of Corpus Annotation: a New Methodological Challenge for Corpus Linguistics. International Journal of Translation, 22 (1).
McEnery, T. & Hardie, A. (2012) Corpus Linguistics. Longman.
Mitkov, R. (2003) The Oxford Handbook of Computational Linguistics. Oxford University Press.
Saint Dizier, P. (2020) The Lexicon of Argumentation for Argument Mining: mehthodological considerations. Anglophona, 29. https://doi.org/10.4000/anglophona.387
Tahmasebi, N. et al. (eds.) (2021) Computational approaches to semantic Change. Language Science Press.
Teaching method
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.
Evaluation method
Evaluation method - Continuous evaluation, including the following components: individual and collective essays, with presentation and discussion in class (40%), and final individual test(60%)
Subject matter
1. Linguistics, computation and natural language processing
1.1 Linguistic modelling, computation and processing
1.2 Computational Linguistics theoretical grounding and strategies
1.3 Products, applications and fields of Computational Linguistics
2. Bases for the processing of natural languages
2.1 Levels of treatment
2.2 Regular Expressions
2.3 Grammars and parsing
3. Strategies and tools for natural languages modelling and processing
3.1 Computational lexicons
3.2 Building and exploring corpora
3.3 Annotation and information/knowledge extraction
Programs
Programs where the course is taught: