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.

翻译：本研究评估了三种不同的爱沙尼亚语词形还原方法——生成式字符级模型、基于模式的词级分类模型以及基于规则的形态分析。根据我们的实验，一个显著更小的生成式模型持续优于基于EstBERT的模式分类模型。此外，我们观察到三种模型所犯错误的交集相对较小，这表明不同方法的集成可能带来改进。