Course Content (Syllabus)
Computational Linguistics constitutes an interdisciplinary area that combines Linguistics, Informatics, Psychology and Cognitive Science. The course’s main aim is to familiarize students with significant recent research questions and theoretical approaches in this field and to provide them access to various tools and applications. We will also demonstrate how linguistic theory is applied to the most up-to-date text processing techniques, word meaning and semantic interpretations.
To this end, significant theoretical topics from Phonology, Morphology, Syntax and Semantics will be re-introduced in the light of computational tools, applications and models. Consequently, we will explore a range of areas, such as Speech Recognition and Synthesis, Grammatical formalizations, Logic, Natural Language Processing and Machine Translation.
Some of the special topics we will discuss include n-grams models, context-free grammars, morphosyntactic tagging, computing with word senses, corpora builders and concordances. Throughout, ample practice with exercises will enable students to use practical tools, corpora and apply various algorithms.
Additional bibliography for study
Jurafsky and Martin (2000, 2007, 2017). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (1st, 2nd, 3rd edition). Prentice Hall. (https://web.stanford.edu/~jurafsky/slp3/)
Roark B. & Sproat R. (2007). Computational Approaches to Morphology and Syntax. Oxford: Oxford University Press.
Manning & Schütze (1999). Foundations of Statistical Natural Language Processing. MIT Press.
Bird S., Klein E. & Loper E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’ Reilly Media.
Καρασίμος, Α. (2011). Υπολογιστική Επεξεργασία της Αλλομορφίας στην Παραγωγή Λέξεων της Νέας Ελληνικής. Διδακτορική Διατριβή. σσ. 305. Πανεπιστήμιο Πατρών: Τμήμα Φιλολογίας. DOI: 10.13140/RG.2.1.1570.7926
Basirat A., Faili H. & Nivre J. (2015). A statistical model for grammar mapping. Natural Language Engineering 22 (2): 215–255.
Goldsmith, J. (2000). Linguistica: An Automatic Morphological Analyzer. In A. Okrent and J. Boyle (Eds.) The Proceedings from the Main Session of the Chicago Linguistic Society's Thirty-sixth Meeting, pp. 1-36.
Hammarström, H. & Borin, L. (2011). Unsupervised learning of morphology. Computational Linguistics, 37(2), pp. 309–350.
Maletti A. (2017). Survey: Finite-state technology in natural language processing. Theoretical Computer Science 679, pp. 2–17.