Course Information
SchoolEnglish Language and Literature
Cycle / Level1st / Undergraduate
Teaching PeriodWinter/Spring
Course ID600004992

Programme of Study: 2018-2019

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
KORMOSElective CoursesWinter/Spring-6

Class Information
Academic Year2019 – 2020
Class PeriodSpring
Instructors from Other Categories
  • Panagiotis Gakis
Weekly Hours3
Total Hours39
Class ID
Course Category
Knowledge Deepening / Consolidation
Mode of Delivery
  • Face to face
Digital Course Content
The course is also offered to exchange programme students.
Language of Instruction
  • English (Instruction, Examination)
Learning Outcomes
Upon completion of this course, students should be able to: • understand basic concepts of Computational Linguistics. • recognize mainstream linguistic theories in a more technical environment. • (computationally) analyze the English and Greek language on different levels. • acquire theoretical and computational skills in language processing. • follow the current trends of an ever-evolving scientific area. • interpret various phenomena by approaching them computationally.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Be critical and self-critical
  • Advance free, creative and causative thinking
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.
basic programming, speech recognition and synthesis, natural language processing, machine translation
Educational Material Types
  • Notes
  • Slide presentations
  • Multimedia
  • Interactive excersises
  • • Computational tools and applications • Python programming tutorial (mock lab)
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Course Organization
Reading Assigment200.8
Written assigments100.4
Student Assessment
10% Participation: Successful completion of the exercises and satisfactory preparation for the final examination are directly dependent on active participation in the lesson and reading of the weekly material. 30% Assignment: There will be one assignment during the course, focusing on a specific topic alongside relevant bibliography, discussion and exercises. 60% Final Exam: The final exam will draw on knowledge accumulated in the course using as its primary source the main coursebook. It will consist of a series of multiple-choice and short-answer questions, identification questions, discussion, apps evaluation, and an essay question concerning specific computational issues and theories.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Formative, Summative)
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Labortatory Assignment (Formative, Summative)
Additional bibliography for study
Main Coursebook 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. ( Supplementary Reading 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 Supplementary Bibliography 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.
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