  This study evaluates three different lemmatization approaches to Estonian --
Generative character-level models, Pattern-based word-level classification
models, and rule-based morphological analysis. According to our experiments, a
significantly smaller Generative model consistently outperforms the
Pattern-based classification model based on EstBERT. Additionally, we observe a
relatively small overlap in errors made by all three models, indicating that an
ensemble of different approaches could lead to improvements.
