Natural Language Processing
Goals
This course aims to introduce students to current applications and possibilities of Natural Language Processing, both speech (audio) and text processing and enable them to be capable to perform basic tasks on natural language processing. The practical sessions of the course will be mainly based on Python 3 and NLTK.
Course plan
1. Introduction to language technologies
2. Text processing
3. Speech processing
The methodology consists of several sessions in the computer lab. There will be theoretical and practical sessions, as well as a combination of both.
Assessment
The continuous assessment is based on class activities and two homework assignments.
The resit will consist in delivering the failed assignments. Only those students who score a minimum of 3 (i.e. ranging from 3 to 4.9) in an assignment can opt for the resit. The only possible final mark after the resit will be 5. The resit takes place a week after the UB evaluation period finishes (January/February).
Examination-based assessment
Under exceptional circumstances only one assessment activity will be set (100% of the final mark).
Bibliography
Books
The Oxford handbook of computational linguistics. Reference and Research Book News, 2003, vol. 18, núm. 3. Portland : Copyright Clearance Center. ISSN 0887-3763.
Hardcastle, W. J., Laver, J., & Gibbon, F. E. (Eds.). (2012). The handbook of phonetic sciences (Vol. 119). John Wiley & Sons
Articles
DE BARCELOS SILVA, A., GOMES, M.M., DA COSTA, C.A., et al., 2020. Intelligent personal assistants: A systematic literature review. Expert systems with applications, vol. 147, pp. 113193-. New York : Elsevier Ltd. ISSN 0957-4174. DOI 10.1016/j.eswa.2020.113193.
Electronic text
Jurafsky, Daniel & Martin, James H. (upcoming) Speech and Language Processing, 3rd edition.
Figueroa, Mauricio (2015). Praat scripting manual for beginners.
Bird, Steven, Ewan Klein, and Edward Loper. Natural Language Processing with Python. O’Reilly, 2009. 978-0-596-51649-9. O’Reilly Media Inc.