# UD-KSL Treebank v1.3: A semi-automated framework for aligning XPOS-extracted units with UPOS tags

arXiv: https://arxiv.org/abs/2506.09009 · HTML: https://mlsanity.com/paper/2506.09009/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [Universal Dependencies for Learner English](https://mlsanity.com/paper/1605.04278/) (2016)
- [Second language Korean Universal Dependency treebank v1.2: Focus on data augmentation and annotation scheme refinement](https://mlsanity.com/paper/2503.14718/) (2025)
- [Span Identification of Epistemic Stance-Taking in Academic Written English](https://mlsanity.com/paper/2306.02038/) (2023)

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

- [Parser agreement and disagreement in L2 Korean UD: Implications for human-in-the-loop annotation](https://mlsanity.com/paper/2605.06625/) (2026)

## Abstract (arXiv)

The present study extends recent work on Universal Dependencies annotations for second-language (L2) Korean by introducing a semi-automated framework that identifies morphosyntactic constructions from XPOS sequences and aligns those constructions with corresponding UPOS categories. We also broaden the existing L2-Korean corpus by annotating 2,998 new sentences from argumentative essays. To evaluate the impact of XPOS-UPOS alignments, we fine-tune L2-Korean morphosyntactic analysis models on datasets both with and without these alignments, using two NLP toolkits. Our results indicate that the aligned dataset not only improves consistency across annotation layers but also enhances morphosyntactic tagging and dependency-parsing accuracy, particularly in cases of limited annotated data.