# RUMLEM: A Dictionary-Based Lemmatizer for Romansh

arXiv: https://arxiv.org/abs/2604.11233 · HTML: https://mlsanity.com/paper/2604.11233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [Unsupervised Morphological Paradigm Completion](https://mlsanity.com/paper/2005.00970/) (2020)
- [The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion](https://mlsanity.com/paper/2005.13756/) (2020)
- [Does mBERT understand Romansh? Evaluating word embeddings using word alignment](https://mlsanity.com/paper/2306.08702/) (2023)
- [The Mediomatix Corpus: Parallel Data for Romansh Language Varieties via Comparable Schoolbooks](https://mlsanity.com/paper/2508.16371/) (2025)
- [Lexicon and Rule-based Word Lemmatization Approach for the Somali Language](https://mlsanity.com/paper/2308.01785/) (2023)
- [Robust Language Identification for Romansh Varieties](https://mlsanity.com/paper/2603.15969/) (2026)

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

- [Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation](https://mlsanity.com/paper/2606.06428/) (2026)

## Abstract (arXiv)

Lemmatization -- the task of mapping an inflected word form to its dictionary form -- is a crucial component of many NLP applications. In this paper, we present RUMLEM, a lemmatizer that covers the five main varieties of Romansh as well as the supra-regional standard variety Rumantsch Grischun. It is based on comprehensive, community-driven morphological databases for Romansh, enabling RUMLEM to cover 77-84% of the words in a typical Romansh text. Since there is a dedicated database for each Romansh variety, an additional application of RUMLEM is variety-aware language classification. Evaluation on 30'000 Romansh texts of varying lengths shows that RUMLEM correctly identifies the variety in 95% of cases. In addition, a proof of concept demonstrates the feasibility of Romansh vs. non-Romansh language classification based on the lemmatizer.