arXiv Computation and Language By Jungyeul Park, KyungTae Lim, Zihao Huang, Eunkyul Leah Jo, Yige Chen, Chulwoo Park

Representing and Parsing Korean Constituency Structure at Different Levels of Granularity

Read the original on arXiv Computation and Language →

The paper investigates how different representations of Korean constituency structure affect parsing performance. It compares three formats—Morpheme+XPOS, Eojeol+XPOS, and Eojeol+UPOS—derived from the Penn Korean Treebank, using gold segmentation and labels to evaluate transition-based parsers. Results show that fine-grained morphological and XPOS information yields the best parsing accuracy, while eojeol-based representations offer shorter transition sequences but lower performance when only UPOS is used.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv Computation and Language
Aug 31

Lexically conditioned realization ambiguity in Korean predicate morphology

The article investigates Korean predicate morphology, showing that a sequence of canonical morphemes and grammatical labels does not uniquely determine the surface form for certain predicates. It demonstrates that identical or nearly identical stem-ending configurations can produce different outputs depending on lexical identity and realization class membership. The study frames this as homonymy with inflectional divergence, highlighting that lexical meaning, subcategorization, and semantic role structure are essential for determining the correct surface realization.

By Wonjun Oh, KyungTae Lim, Jungyeul Park
arXiv AI
Sep 18

KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologisms

KoNeoBench is a curated dataset designed to evaluate large language models’ understanding of Korean neologisms. It contains 1,785 recently attested Korean words from online news since 2020, each accompanied by usage examples, word‑formation analyses, and dictionary‑style definitions. The authors define four evaluation tasks, report results from recent models and a human baseline, and find that current LLMs struggle with recovering source components, distinguishing semantic categories, and generating accurate definitions.

By Soha Lee, Soojin Lee, Heesung Yang, Hyunju Song, Hyunji Lee, Jinsan An, Jeongwan Shin, Jin Hyun Park, Jun Lee, Hyeyoung Park, Kilim Nam