P127 Machine Translation Techniques

Faculty of Informatics
Spring 2001
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Shun Ha Sylvia Wong, Ph.D. (lecturer)
Guaranteed by
prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: Shun Ha Sylvia Wong, Ph.D.
Timetable
Tue 8:00–9:50 A107
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Syllabus
  • Introduction to Problems of Machine Translation: different kinds of ambiguities (i.e. lexical & structural ambiguities), different realizations of words between source and target languages, non-existence of source to target equivalence, meaning representation, speed of processing (efficiency), modularity and maintainability. Based on some example MT systems, this course aims at discussing the characteristics, strengths and weaknesses for the following approaches: Direct & Indirect MT Systems (e.g. Systran), Sublanguage Approach (e.g. Météo), Linguistic-based MT (e.g. MT with LFG, Shake-and-bake MT), Knowledge-based MT (e.g. the KANT system), Example-based MT, and Statistical MT (e.g. the Candide system).
Literature
  • http://clwww.essex.ac.uk/~doug/MTbook
  • HUTCHINS, W. John and Harold L. SOMERS. An introduction to machine translation. London: Academic Press, 1992, xxi, 362 s. ISBN 0-12-362830-X. info
Assessment methods (in Czech)
lecture, examination
Language of instruction
English
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/usr/wong/teaching/mt/P127.html
The course is also listed under the following terms Spring 2000.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2001/P127