UD-KSL Treebank v1.3: A semi-automated framework for aligning XPOS-extracted units with UPOS tags
This work addresses annotation consistency and parsing accuracy for second-language Korean learners, representing an incremental improvement over existing Universal Dependencies frameworks.
The researchers tackled the problem of aligning XPOS-extracted morphosyntactic constructions with UPOS tags for second-language Korean, introducing a semi-automated framework and expanding the corpus with 2,998 new sentences. Their results showed that the aligned dataset improved consistency across annotation layers and enhanced morphosyntactic tagging and dependency-parsing accuracy, especially with limited annotated data.
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.