UMR annotation of Chinese Verb compounds and related constructions

Haibo Sun, Yifan Zhu, Jin Zhao, Nianwen Xue


Abstract
This paper discusses the challenges of annotating the predicate-argument structure of Chinese verb compounds in Uniform Meaning Representation (UMR), a recent meaning representation framework that extends Abstract Meaning Representation (AMR) to cross-linguistic settings. The key issue is to decide whether to annotate the argument structure of a verb compound as a whole, or to annotate the argument structure of their component verbs as well as the relations between them. We examine different types of Chinese verb compounds, and propose how to annotate them based on the principle of compositionality, level of grammaticalization, and productivity of component verbs. We propose a solution to the practical problem of having to define the semantic roles for Chinese verb compounds that are quite open-ended by separating compositional verb compounds from verb compounds that are non-compositional or have grammaticalized verb components. For compositional verb compounds, instead of annotating the argument structure of the verb compound as a whole, we annotate the argument structure of the component verbs as well as the semantic relations between them as creating an exhaustive list of such verb compounds is infeasible. Verb compounds with grammaticalized verb components also tend to be productive and we represent grammaticalized verb compounds as either attributes of the primary verb or as relations.
Anthology ID:
2023.cxgsnlp-1.9
Volume:
Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)
Month:
March
Year:
2023
Address:
Washington, D.C.
Editors:
Claire Bonial, Harish Tayyar Madabushi
Venues:
CxGsNLP | SyntaxFest
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
75–84
Language:
URL:
https://aclanthology.org/2023.cxgsnlp-1.9
DOI:
Bibkey:
Cite (ACL):
Haibo Sun, Yifan Zhu, Jin Zhao, and Nianwen Xue. 2023. UMR annotation of Chinese Verb compounds and related constructions. In Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023), pages 75–84, Washington, D.C.. Association for Computational Linguistics.
Cite (Informal):
UMR annotation of Chinese Verb compounds and related constructions (Sun et al., CxGsNLP-SyntaxFest 2023)
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PDF:
https://aclanthology.org/2023.cxgsnlp-1.9.pdf