PV173 Seminary on Natural Language Processing

Faculty of Informatics
Autumn 2009
Extent and Intensity
0/2. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: k (colloquium). Other types of completion: z (credit).
Teacher(s)
doc. RNDr. Aleš Horák, Ph.D. (lecturer)
doc. Mgr. Pavel Rychlý, Ph.D. (lecturer)
doc. RNDr. Petr Sojka, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Wed 10:00–11:50 B411
Prerequisites
SOUHLAS
Active work in the Laboratory of Natural language processing as well as an approval of registration by the lecturer (P.Rychly, A.Horak) is needed.
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
there are 44 fields of study the course is directly associated with, display
Course objectives
The aim of the seminar is a presentation of results of student research (both doctoral and pregradual) in the NLP Laboratory (http://nlp.fi.muni.cz/).
Syllabus
  • The lectures consist mostly of students' presentations. The students can control the content of the seminar in the discussions after each presentation.
Literature
  • The Oxford handbook of computational linguistics. Edited by Ruslan Mitkov. Oxford: Oxford University Press, 2003, xx, 784. ISBN 0198238827. info
  • MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
Teaching methods
Students presentations, discussion.
Assessment methods
Students must attend the seminar regularly and present their own work. Credits are assigned to students according to the presented results.
Language of instruction
Czech
Further Comments
The course is taught each semester.
Teacher's information
http://nlp.fi.muni.cz/cs/Laboratorni_seminar
The course is also listed under the following terms Spring 2005, Autumn 2005, Spring 2006, Autumn 2006, Spring 2007, Autumn 2007, Spring 2008, Autumn 2008, Spring 2009, Spring 2010, Autumn 2010, Spring 2011, Autumn 2011, Spring 2012, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, Autumn 2017, Spring 2018, Autumn 2018, Spring 2019, Autumn 2019, Spring 2020, Autumn 2020, Spring 2021, Autumn 2021, Spring 2022, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.
  • Enrolment Statistics (Autumn 2009, recent)
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