# Joint Lemmatization and Morphological Tagging with LEMMING

arXiv: https://arxiv.org/abs/2405.18308 · HTML: https://mlsanity.com/paper/2405.18308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 33 references that are in the map.
- First posted on arXiv 28 May 2024.

## Built on by (most cited first, 6 of 6)

- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [A State-of-the-Art Morphosyntactic Parser and Lemmatizer for Ancient Greek](https://mlsanity.com/paper/2410.12055/) (2024)
- [The UD-NewsCrawl Treebank: Reflections and Challenges from a Large-scale Tagalog Syntactic Annotation Project](https://mlsanity.com/paper/2505.20428/) (2025)
- [Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://mlsanity.com/paper/2506.18399/) (2025)
- [Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data](https://mlsanity.com/paper/2510.07434/) (2025)
- [Investigating Large Language Models' Linguistic Abilities for Text Preprocessing](https://mlsanity.com/paper/2510.11482/) (2025)

## Abstract (arXiv)

We present LEMMING, a modular log-linear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. LEMMING sets the new state of the art in token-based statistical lemmatization on six languages; e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05 to 1.58. We also give empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial.