Comparison of Current Approaches to Lemmatization: A Case Study in Estonian
Abstract
The study compares generative character-level models, pattern-based word-level classification using EstBERT, and rule-based morphological analysis for lemmatization in Estonian, showing that a smaller generative model outperforms the pattern-based model and highlighting the potential benefits of ensemble approaches.
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.
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