Department: MSc in Informatics
Module Description: This is an introductory course that presumes no prior familiarity with Computational Linguistics. This course provides an introduction to the basic theory and practice of computational approaches to natural language processing. The module cover the following topic: introduction to programming in Python & NLTK, tokenization, part-of-speech tagging, context-free grammars for natural language, evaluating a natural language processing system, parsing techniques, information extraction, Arabic language processing. The course also provides an introductory insight into the state of current research in Computational Linguistics, including AI and Data Science techniques.
Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied text analysis with Python: enabling language-aware data products with machine learning. O’Reilly Media.
Dickinson, M., Brew, C., & Meurers, D. (2013). Language and computers. Wiley-Blackwell.
Ghavami, P. (2020). Big data analytics methods : analytics techniques in data mining, deep learning and natural language processing. 2nd edn. De Gruyter.
Goldberg, Y., and Hirst, G. (2017). Neural network methods in natural language processing. Morgan and Claypool publishers.
Ingersoll, G. S., Morton, T. S., & Farris, A. L. (2013). Taming text: how to find, organize, and manipulate it. Manning Publications.
Koehn, P. (2010). Statistical machine translation. Cambridge University Press.
Kulkarni, A. and Shivananda, A. (2020). Python natural language processing projects: build next-generation language applications for analyzing varied texts using python libraries. Packt Publishing.
Lane, H., & Dyshel, M. (2024). Natural language processing in action. 2nd edn. Manning Publications.
Lutz, M. & Ascher, D. (2013). Learning python. 5th edn. Sebastool, CA: O'Reilly.
Matthes, E. (2023). Python crash course: a hands-on, project-based introduction to programming. 3rd edn. No Starch Press.
Mùˆller, A. C. ;. G. S. (2020). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media.
Silge, J., & Robinson, D. (2017). Text mining with R: a tidy approach. O’Reilly Media.
Wilcock, G. (2009). Introduction to linguistic annotation and text analytics. Springer.
