Natural Language Processing

Code
570524
Credits
5cr

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

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

Miltkov, Ruslan (ed). (2003) The Oxford Handbook of Computational Linguistics. Oxford University Press.  Enllaç

Hardcastle, W. J., Laver, J., & Gibbon, F. E. (Eds.). (2012). The handbook of phonetic sciences (Vol. 119). John Wiley & Sons  Enllaç

de Barcelos Silva, A., Gomes, M. M., da Costa, C. A., da Rosa Righi, R., Barbosa, J. L. V., Pessin, G., ... & Federizzi, G. (2020). Intelligent personal assistants: A systematic literature review. Expert Systems with Applications, 147, 113193.

Jurafsky, Daniel & Martin, James H. (upcoming) Speech and Language Processing, 3rd edition.  Enllaç

Figueroa, Mauricio (2015). Praat scripting manual for beginners.  Enllaç

Bird, Steven, Ewan Klein, and Edward Loper. Natural Language Processing with Python. O’Reilly, 2009. 978-0-596-51649-9. O’Reilly Media Inc.   Enllaç