When the Misidentified Adverbial Phrase Functions as a Complement

Yige Chen, Kyuwon Kim, Kyung Tae Lim, Jungyeul Park, Chulwoo Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study investigates the predicate-argument structure in Korean language processing. Despite the importance of distinguishing mandatory arguments and optional modifiers in sentences, research in this area has been limited. We introduce a dataset with token-level annotations which labels mandatory and optional elements as complements and adjuncts, respectively. Particularly, we reclassify certain Korean phrases, previously misidentified as adverbial phrases, as complements, addressing misuses of the term adjunct in existing Korean treebanks. Utilizing a Korean dependency treebank, we develop an automatic labeling technique for complements and adjuncts. Experiments using the proposed dataset yield satisfying results, demonstrating that the dataset is trainable and reliable.

Original languageEnglish
Title of host publicationEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024
EditorsYaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
PublisherAssociation for Computational Linguistics (ACL)
Pages12326-12336
Number of pages11
ISBN (Electronic)9798891761681
DOIs
StatePublished - 2024
Event2024 Findings of the Association for Computational Linguistics, EMNLP 2024 - Hybrid, Miami, United States
Duration: 12 Nov 202416 Nov 2024

Publication series

NameEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024

Conference

Conference2024 Findings of the Association for Computational Linguistics, EMNLP 2024
Country/TerritoryUnited States
CityHybrid, Miami
Period12/11/2416/11/24

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