Abstract
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.
Abstract (translated)
本研究评估了三种不同的词素化方法：基于生成特征的模型、基于模式的单词级别分类模型和基于规则的形态分析。根据我们的实验结果，一个显著较小的生成模型在基于EstBERT的条件下始终优于基于模式的分类模型。此外，我们观察到三种模型所犯的错误相对较小，这表明不同的方法集可能会带来改进。
URL
https://arxiv.org/abs/2404.15003
