# Yet Another Format of Universal Dependencies for Korean

arXiv: https://arxiv.org/abs/2209.09742 · HTML: https://mlsanity.com/paper/2209.09742/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)

## Built on by (most cited first, 6 of 6)

- [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)
- [Enhancing Korean Dependency Parsing with Morphosyntactic Features](https://mlsanity.com/paper/2503.21029/) (2025)
- [Constituency Structure over Eojeol in Korean Treebanks](https://mlsanity.com/paper/2512.22487/) (2025)
- [Unlocking Korean Verbs: A User-Friendly Exploration into the Verb Lexicon](https://mlsanity.com/paper/2410.01100/) (2024)
- [Representing and Parsing Korean Constituency Structure at Different Levels of Granularity](https://mlsanity.com/paper/2608.27035/) (2026)

## Abstract (arXiv)

In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies. We present the linguistic rationale that illustrates the motivation and the necessity of adopting the morpheme-based format, and develop scripts that convert between the original format used by Universal Dependencies and the proposed morpheme-based format automatically. The effectiveness of the proposed format for Korean dependency parsing is then testified by both statistical and neural models, including UDPipe and Stanza, with our carefully constructed morpheme-based word embedding for Korean. morphUD outperforms parsing results for all Korean UD treebanks, and we also present detailed error analyses.