Analysis of Automatic Stress Assignment in Slovene

Article type: Research Article

Authors: Marinčič, Domen | Tušar, Tea | Gams, Matjaž | Šef, Tomaž

Affiliations: Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia, e-mail: {domen.marincic, tea.tusar, matjaz.gams, tomaz.sef}@ijs.si

Abstract: We tested the ability of humans and machines (data mining techniques) to assign stress to Slovene words. This is a challenging comparison for machines since humans accomplish the task outstandingly even on unknown words without any context. The goal of finding good machine-made models for stress assignment was set by applying new methods and by making use of a known theory about rules for stress assignment in Slovene. The upgraded data mining methods outperformed expert-defined rules on practically all subtasks, thus showing that data mining can more than compete with humans when constructing formal knowledge about stress assignment is concerned. Unfortunately, compared to humans directly, the data mining methods still failed to achieve as good results as humans on assigning stress to unknown words.

Keywords: stress assignment, data mining, expert knowledge

Journal: Informatica, vol. 20, no. 1, pp. 35-50, 2009