# Enhancing Korean Dependency Parsing with Morphosyntactic Features

arXiv: https://arxiv.org/abs/2503.21029 · HTML: https://mlsanity.com/paper/2503.21029/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Empirical Study of Zero-Shot NER with ChatGPT](https://mlsanity.com/paper/2310.10035/) (2023)
- [Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean](https://mlsanity.com/paper/2403.10882/) (2024)
- [Yet Another Format of Universal Dependencies for Korean](https://mlsanity.com/paper/2209.09742/) (2022)
- [ChatGPT is a Potential Zero-Shot Dependency Parser](https://mlsanity.com/paper/2310.16654/) (2023)
- [K-UniMorph: Korean Universal Morphology and its Feature Schema](https://mlsanity.com/paper/2305.06335/) (2023)
- [K-UD: Revising Korean Universal Dependencies Guidelines](https://mlsanity.com/paper/2412.00856/) (2024)
- [Korean Named Entity Recognition Based on Language-Specific Features](https://mlsanity.com/paper/2305.06330/) (2023)

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

- [Construction and educational application of a linguistically grounded dependency treebank for Uyghur](https://mlsanity.com/paper/2507.21536/) (2025)

## Abstract (arXiv)

This paper introduces UniDive for Korean, an integrated framework that bridges Universal Dependencies (UD) and Universal Morphology (UniMorph) to enhance the representation and processing of Korean {morphosyntax}. Korean's rich inflectional morphology and flexible word order pose challenges for existing frameworks, which often treat morphology and syntax separately, leading to inconsistencies in linguistic analysis. UniDive unifies syntactic and morphological annotations by preserving syntactic dependencies while incorporating UniMorph-derived features, improving consistency in annotation. We construct an integrated dataset and apply it to dependency parsing, demonstrating that enriched morphosyntactic features enhance parsing accuracy, particularly in distinguishing grammatical relations influenced by morphology. Our experiments, conducted with both encoder-only and decoder-only models, confirm that explicit morphological information contributes to more accurate syntactic analysis.