Goals and Objectives
and you know how to take these differences into account when designing computational models of morphology.
Description of the Training Materials
|(Sub)discipline, topic, language(s)||
computational morphology, finite-state methods
morphology, weighted finite-state networks, two-level rules, xfst, lexc, twolc
Kielipankki morphologies: http://urn.fi/urn:nbn:fi:lb-2018041703
|Structure and duration||The course comprises seven lectures, based on the original course. The lectures are tutorial-like, showing how HFST tools can be used for implementing finite-state morphologies. There are assignments at the end of each lecture. This is a self-study course, so there are no time limits. Solutions to the assignments are not available, since the original course is still lectured each year.|
|Target audience||Prerequisites include foundations of general linguistics and basic knowledge on how to use a computer. Some programming experience is desirable. Knowledge of Natural Language Processing ( ) is also a plus.|
The course is accessible also at https://notebooks.csc.fi. Logging to the service requires Haka or CSC account or visitor account via CSC/SAFMORIL helpdesk.
Self-study course containing seven lectures implemented as Jupyter notebooks. Tutorial-type lectures with assignments at the end.
|Course(s) in which the training material was used||The training material is taught as part of the “Computational Morphology” (LDA-T302) by Mathias Creutz in the Master’s programme Linguistic Diversity and Digital Humanities at the University of Helsinki for 5 ECTS.|
|Licence and (re)use||CC BY 4.0: https://creativecommons.org/licenses/by/4.0/|
First GitHub repository commit, Nov 29, 2018
|Last modification date||A stable release was made on 25 June 2021|
Experience with Using CLARIN Resources in Teaching
Additional Information and Resources
See the info page of the course: http://urn.fi/urn:nbn:fi:lb-2021053003