Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages

Abstract

This paper describes an initial set of experiments in data-driven morphological analysis of Uralic languages. The paper differs from previous work in that our work covers both lemmatization and generating ambiguous analyses. While hand-crafted finite-state transducers represent the state of the art in morphological analysis for most Uralic languages, we believe that there is a place for datadriven approaches, especially with respect to making up for lack of completeness in the шlexicon. We present results for nine Uralic languages that show that, at least for basic nominal morphology for six out of the nine languages, data-driven methods can achieve an F-score of over 90%, providing results that approach those of finite-state techniques. We also compare our system to an earlier approach to Finnish data-driven morphological analysis (Silfverberg and Hulden, 2018) and show that our system outperforms this baseline.