Computational Morphology with HFST

This open educational resource can be used as self-study material or as a classroom tutorial. It demonstrates how Helsinki Finite-State Technology (HFST) tools can generate finite-state morphologies. Through practical exercises, students will learn how to use finite-state methods to develop a morphology for a language. This online course is suitable as a complement to a more theory or linguistics-oriented course on morphology. 

Erik Axelson developed a Jupyter Notebook interactive version based on an earlier version of the Computational Morphology course taught by Mathias Creutz and Senka Drobac in the Master’s programme 'Linguistic Diversity and Digital Humanities' at the University of Helsinki. 

Learning Outcomes

After successfully completing the course, learners will be able to:
- Explain the basic theory of finite-state automata and transducers,
- Design morphological lexica using finite-state technology,
- Write morpho-phonological rules in a finite-state framework,
- Describe the diversity of morphological structures in different languages and apply them when designing computational morphology models.


Role: Jupyter code developer of course material
Department of Digital Humanities / The Language Bank of Finland (FIN-CLARIN)
Faculty of Arts, University of Helsinki
Helsinki, Finland

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

URL (s) to Resource
Resource URL Type

CLARIN Language Resources


Kielipankki morphologies:

Structure and Duration The course comprises seven lectures, based on the original course, with assignments. The solutions to the assignments are unavailable since the original course is still lectured each year.
Target Audience MA students in linguistics or digital humanities

Expertise (Skill) Level

Intermediate/advanced level
Prerequisites include:
Facilities Required

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:
Creation Date

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

Students appreciated the availability of XFST and TWOL tools through the Python application interface. It facilitates learning and makes integrating morphological analysis and generation as part of larger natural language processing software easy.

Cite this Work

Axelson, Creutz, Drobac (2019). Computational Morphology with HFST – an adaptation of the teaching material of the course 'Computational Morphology' taught by Mathias Creutz in the Master’s programme 'Linguistic Diversity and Digital Humanities' at the University of Helsinki. [Learning material]. Language Bank of Finland. Accessible at

Contact Information

Teachers who reuse and adapt this training material can share their feedback via