# Context based lemmatizer for Polish language

arXiv: https://arxiv.org/abs/2207.11565 · HTML: https://mlsanity.com/paper/2207.11565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 23 Jul 2022.

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

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages](https://mlsanity.com/paper/1706.09031/) (2017)
- [LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs](https://mlsanity.com/paper/1808.03703/) (2018)
- [Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks](https://mlsanity.com/paper/1902.00972/) (2019)

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

- [BanLemma: A Word Formation Dependent Rule and Dictionary Based Bangla Lemmatizer](https://mlsanity.com/paper/2311.03078/) (2023)

## Abstract (arXiv)

Lemmatization is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. In computational linguistics, lemmatisation is the algorithmic process of determining the lemma of a word based on its intended meaning. Unlike stemming, lemmatisation depends on correctly identifying the intended part of speech and meaning of a word in a sentence, as well as within the larger context surrounding that sentence. As a result, developing efficient lemmatisation algorithm is the complex task. In recent years it can be observed that deep learning models used for this task outperform other methods including machine learning algorithms. In this paper the polish lemmatizer based on Google T5 model is presented. The training was run with different context lengths. The model achieves the best results for polish language lemmatisation process.