mlsanityData to 7 Oct 2026

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

arXiv 2404.15003 ↗First posted 23 Apr 2024cs.CLcited by 1

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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

  1. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  2. 2020Applying the Transformer to Character-level Transductioncited by 43
  3. 2020EstBERT: A Pretrained Language-Specific BERT for Estoniancited by 16
  4. 2019UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Mergingcited by 15

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Citing papers per year: 2024: 1.

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