# LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs

arXiv: https://arxiv.org/abs/1808.03703 · HTML: https://mlsanity.com/paper/1808.03703/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://mlsanity.com/paper/2105.11314/) (2021)
- [SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes](https://mlsanity.com/paper/2302.09527/) (2023)
- [A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit](https://mlsanity.com/paper/2208.10310/) (2022)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Context based lemmatizer for Polish language](https://mlsanity.com/paper/2207.11565/) (2022)
- [Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://mlsanity.com/paper/2506.18399/) (2025)
- [Investigating Large Language Models' Linguistic Abilities for Text Preprocessing](https://mlsanity.com/paper/2510.11482/) (2025)

## Abstract (arXiv)

We present LemmaTag, a featureless neural network architecture that jointly generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. We demonstrate that both tasks benefit from sharing the encoding part of the network, predicting tag subcategories, and using the tagger output as an input to the lemmatizer. We evaluate our model across several languages with complex morphology, which surpasses state-of-the-art accuracy in both part-of-speech tagging and lemmatization in Czech, German, and Arabic.