UD - KSL Treebank v1.3: A semi-automated framework for aligning XPOS -extracted units with UPOS tags (2025)

Abstract: 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.

## introduction ongoing efforts to develop linguistic annotations for learner corpora have produced valuable resources that support quantitative, targeted analyses of specific linguistic features (e.g., argument structure constructions: sung and kyle, 2024, stancetaking features: eguchi and kyle, 2023, grammatical errors: @xcite . one such initiative focuses on morphosyntactic features, including part-of-speech (pos) categories and dependency relations, thereby allowing for more fine-grained investigations on linguistic structures produced by learners @xcite . these investigations can inform theoretical models of language development and improve empirical approaches to evaluating learner performance. in parallel, many learner corpora follow the universal dependencies (ud) framework, providing cross-linguistic consistency in grammatical structures via universal pos and dependency tags @xcite @xcite @xcite . notably, second language (l2) korean has recently been incorporated into this growing body of ud-annotated learner corpora @xcite @xcite . previous research on ud annotations for l2 korean has produced expertcurated resources with detailed xpos tags from the korean-specific sejong set, enabling fine-grained morphosyntactic feature extraction. in contrast, the corresponding universal pos (upos) tags in these corpora were typically generated automaticallyusing a domain-general korean analysis package (e.g., stanza-gsd; @xcite -with minimal human validation @xcite . this disparity in annotation procedures may lead to inconsistencies, potentially undermining the dataset's internal reliability and reducing the accuracy of downstream applications. 1 to address this gap, this study extends recent l2-korean ud work @xcite by introducing a semi-automated framework that aligns xpos tags with upos categories, combining automation with targeted human validation. this framework is informed by the structure of korean eojeol-a morphosyntactic unit defined by whitespace segmentation-and explains how different morphemes combine to form specific morphosyntactic categories.we also expand the l2-korean corpus with 2,998 newly annotated sentences from argumentative essays. to assess the benefits of xpos-upos alignment on model performance, we fine-tune l2-korean morphosyntactic analysis models on datasets with and without this alignment using two nlp toolkits. results show that alignment improves tagging and dependency parsing accuracy, particularly in lowresource settings-likely due to greater consistency between upos tags and syntactic dependencies. ## experiments we conducted experiments to assess the impact xpos-upos alignment on model performance using a 2×2×2 design. the factors were: dataset type (ud-ksl v1.2 vs. ud-ksl working set); refinement type (aligned [a dataset in which upos tags were aligned with corresponding xpos tags] vs. unaligned); and toolkit type (spacy vs. trankit). l2-korean morphosyntactic analysis models were fine-tuned on both dataset versions with both toolkits to determine whether the xpos-upos alignment enhance the accuracy of morphosyntactic parsing and tagging in l2-korean data. ## conclusion building upon prior l2-korean ud annotation efforts @xcite @xcite , the present work introduced a semi-automatic framework for aligning fine-grained xpos tags with upos tags for (l2-)korean treebanks. we also augmented the ud-ksl treebank by annotating 2,998 new sentences from an argumentative writing domain. to support reproducibility and promote further research in l2 korean nlp, all relevant resources have been made publicly available via the ud-ksl treebank: https: //github.com/universaldependencies/ud_ korean-ksl/tree/dev. we evaluated the effect of xpos-upos align-ment by training models both with and without alignment across two open-access nlp toolkits. alignment consistently improved tagging accuracy for upos, xpos, and lemma. however, dependency-parsing gains varied by toolkit and dataset size: on the smaller annotated dataset, the transformer-based trankit showed more pronounced improvements than spacy; on the larger dataset, alignment yielded minimal parsing gains for both toolkits, although trankit still outperformed spacy overall. these results suggest that the alignment enhances tagging robustness, while transformer architectures strengthen contextual parsing. conversely, spacy's dictionary-driven hybrid lemmatizer outperformed trankit in lemma generation, suggesting that integrating lexiconbased methods could further improve lemmatization accuracy. overall, this semi-automated alignment supports more consistent upos annotations and robust morphosyntactic analysis in l2 korean nlp research.