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

The present study extends recent work on Universal Dependencies annotations for secondlanguage (L2) Korean by introducing a semiautomated 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.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.law-1.9
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Gyu‐Ho Shin, Boo Kyung Jung, Chanyoung Lee, You Kyung Sung et al.
Natural Language Processing Techniques
article

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

Gyu‐Ho Shin, Boo Kyung Jung, Chanyoung Lee, You Kyung Sung, Hakyung Sung
article en

Abstract

The present study extends recent work on Universal Dependencies annotations for secondlanguage (L2) Korean by introducing a semiautomated 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.

Academy of Korean Studies
Openalex Percentile: Top 90%
Natural Language Processing Techniques
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