Comparison of Current Approaches to Lemmatization: A Case Study in Estonian
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 23 Apr 2024.
Builds on
4 of 13 references.
References · 4
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
- 2020Applying the Transformer to Character-level Transductioncited by 43
- 2020EstBERT: A Pretrained Language-Specific BERT for Estoniancited by 16
- 2019UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Mergingcited by 15
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Citing papers per year: 2024: 1.
1 paper.