Representing and Parsing Korean Constituency Structure at Different Levels of Granularity
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Abstract (arXiv)
Korean constituency parsing raises a representational challenge because the terminal units of a phrase-structure tree do not straightforwardly correspond to simple surface words. Korean eojeols are morphologically complex spacing units, and existing constituency resources differ in how they represent eojeol-internal morphology and non-overt elements. This paper compares three constituency parsing representations derived from the Penn Korean Treebank: Morpheme+XPOS, Eojeol+XPOS, and Eojeol+UPOS. We construct these representations by removing null elements, aligning Penn Korean phrase structure with overt eojeol tokens, preserving Penn Korean phrase labels where possible, and varying the terminal and preterminal layers. We then evaluate canonical non-binary transition-based constituency parsers in top-down, in-order, and bottom-up orders under a shared modeling and evaluation setup. All experiments use gold terminal segmentation and gold preterminal labels and therefore evaluate constituency parsing conditioned on gold morphosyntactic annotation. Eojeol terminals yield shorter transition sequences, but Eojeol+UPOS parsing substantially underperforms the morphologically richer conditions. Eojeol+XPOS narrows this gap, while Morpheme+XPOS gives the strongest results even after its predictions are projected to the eojeol terminal domain. Under these gold-annotation conditions, the results show that fine-grained morphological and XPOS representations provide valuable evidence for the evaluated parsers. This empirical finding concerns the information available for parsing and does not by itself determine the linguistically preferable terminal domain. Independently, linguistic and resource-design considerations motivate eojeol as a stable and interpretable surface domain for phrase-structure annotation, with morpheme-level and XPOS information retained as aligned morphosyntactic evidence.
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References · 14
- 2018Constituency Parsing with a Self-Attentive Encodercited by 199
- 2018Multilingual Constituency Parsing with Self-Attention and Pre-Trainingcited by 117
- 2021KLUE: Korean Language Understanding Evaluationcited by 114
- 2016Recurrent Neural Network Grammarscited by 109
- 2014Grammar as a Foreign Languagecited by 70
- 2017In-Order Transition-based Constituent Parsingcited by 23
- 2019Cross-Domain Generalization of Neural Constituency Parserscited by 17
- 2016What Do Recurrent Neural Network Grammars Learn About Syntax?cited by 15
- 2018Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategycited by 8
- 2022Yet Another Format of Universal Dependencies for Koreancited by 6
- 2024K-UD: Revising Korean Universal Dependencies Guidelinescited by 4
- 2023Korean Named Entity Recognition Based on Language-Specific Featurescited by 3
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- 2023Word segmentation granularity in Koreancited by 2
- 2025Constituency Structure over Eojeol in Korean Treebankscited by 1
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