# Comparison of Current Approaches to Lemmatization: A Case Study in Estonian

arXiv: https://arxiv.org/abs/2404.15003 · HTML: https://mlsanity.com/paper/2404.15003/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Applying the Transformer to Character-level Transduction](https://mlsanity.com/paper/2005.10213/) (2020)
- [EstBERT: A Pretrained Language-Specific BERT for Estonian](https://mlsanity.com/paper/2011.04784/) (2020)
- [UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging](https://mlsanity.com/paper/1908.06931/) (2019)

## Built on by (most cited first, 1 of 1)

- [GliLem: Leveraging GliNER for Contextualized Lemmatization in Estonian](https://mlsanity.com/paper/2412.20597/) (2024)

## 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.