# Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model

arXiv: https://arxiv.org/abs/2112.09860 · HTML: https://mlsanity.com/paper/2112.09860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [UniMorph 2.0: Universal Morphology](https://mlsanity.com/paper/1810.11101/) (2018)
- [Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices](https://mlsanity.com/paper/2112.03572/) (2021)
- [An Unsupervised Method for Uncovering Morphological Chains](https://mlsanity.com/paper/1503.02335/) (2015)
- [Evaluating Neural Morphological Taggers for Sanskrit](https://mlsanity.com/paper/2005.10893/) (2020)
- [Neural Morphological Tagging from Characters for Morphologically Rich Languages](https://mlsanity.com/paper/1606.06640/) (2016)
- [Modeling Composite Labels for Neural Morphological Tagging](https://mlsanity.com/paper/1810.08815/) (2018)

## Built on by (most cited first, 3 of 3)

- [Why do language models perform worse for morphologically complex languages?](https://mlsanity.com/paper/2411.14198/) (2024)
- [UniMorph 4.0: Universal Morphology](https://mlsanity.com/paper/2205.03608/) (2022)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)

## Abstract (arXiv)

Developing Natural Language Processing resources for a low resource language is a challenging but essential task. In this paper, we present a Morphological Analyzer for Gujarati. We have used a Bi-Directional LSTM based approach to perform morpheme boundary detection and grammatical feature tagging. We have created a data set of Gujarati words with lemma and grammatical features. The Bi-LSTM based model of Morph Analyzer discussed in the paper handles the language morphology effectively without the knowledge of any hand-crafted suffix rules. To the best of our knowledge, this is the first dataset and morph analyzer model for the Gujarati language which performs both grammatical feature tagging and morpheme boundary detection tasks.