Seokwon Park


2020

pdf bib
Enhancing Quality of Corpus Annotation: Construction of the Multi-Layer Corpus Annotation and Simplified Validation of the Corpus Annotation
Youngbin Noh | Kuntae Kim | Minho Lee | Cheolhun Heo | Yongbin Jeong | Yoosung Jeong | Younggyun Hahm | Taehwan Oh | Hyonsu Choe | Seokwon Park | Jin-Dong Kim | Key-Sun Choi
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

pdf bib
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
Tae Hwan Oh | Ji Yoon Han | Hyonsu Choe | Seokwon Park | Han He | Jinho D. Choi | Na-Rae Han | Jena D. Hwang | Hansaem Kim
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD corpora, we follow the UDv2 guidelines, and extensively revise the part-of-speech tags and the dependency relations to reflect morphological features and flexible word- order aspects in Korean. The original and the revised versions of PKT-UD are experimented with transformer-based parsing models using biaffine attention. The parsing model trained on the revised corpus shows a significant improvement of 3.0% in labeled attachment score over the model trained on the previous corpus. Our error analysis demonstrates that this revision allows the parsing model to learn relations more robustly, reducing several critical errors that used to be made by the previous model.