# Representing and Parsing Korean Constituency Structure at Different Levels of Granularity

arXiv: https://arxiv.org/abs/2608.27035 · HTML: https://mlsanity.com/paper/2608.27035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [Grammar as a Foreign Language](https://mlsanity.com/paper/1412.7449/) (2014)
- [In-Order Transition-based Constituent Parsing](https://mlsanity.com/paper/1707.05000/) (2017)
- [Cross-Domain Generalization of Neural Constituency Parsers](https://mlsanity.com/paper/1907.04347/) (2019)
- [What Do Recurrent Neural Network Grammars Learn About Syntax?](https://mlsanity.com/paper/1611.05774/) (2016)
- [Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategy](https://mlsanity.com/paper/1804.07961/) (2018)
- [Yet Another Format of Universal Dependencies for Korean](https://mlsanity.com/paper/2209.09742/) (2022)
- [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)
- [Word segmentation granularity in Korean](https://mlsanity.com/paper/2309.03713/) (2023)
- [Constituency Structure over Eojeol in Korean Treebanks](https://mlsanity.com/paper/2512.22487/) (2025)

## Abstract (arXiv)

Korean constituency parsing raises a representational challenge because the terminal units of a phrase-structure tree do not straightforwardly correspond to simple surface words. Korean eojeols are morphologically complex spacing units, and existing constituency resources differ in how they represent eojeol-internal morphology and non-overt elements. This paper compares three constituency parsing representations derived from the Penn Korean Treebank: Morpheme+XPOS, Eojeol+XPOS, and Eojeol+UPOS. We construct these representations by removing null elements, aligning Penn Korean phrase structure with overt eojeol tokens, preserving Penn Korean phrase labels where possible, and varying the terminal and preterminal layers. We then evaluate canonical non-binary transition-based constituency parsers in top-down, in-order, and bottom-up orders under a shared modeling and evaluation setup. All experiments use gold terminal segmentation and gold preterminal labels and therefore evaluate constituency parsing conditioned on gold morphosyntactic annotation. Eojeol terminals yield shorter transition sequences, but Eojeol+UPOS parsing substantially underperforms the morphologically richer conditions. Eojeol+XPOS narrows this gap, while Morpheme+XPOS gives the strongest results even after its predictions are projected to the eojeol terminal domain. Under these gold-annotation conditions, the results show that fine-grained morphological and XPOS representations provide valuable evidence for the evaluated parsers. This empirical finding concerns the information available for parsing and does not by itself determine the linguistically preferable terminal domain. Independently, linguistic and resource-design considerations motivate eojeol as a stable and interpretable surface domain for phrase-structure annotation, with morpheme-level and XPOS information retained as aligned morphosyntactic evidence.