An Argument Structure Construction Treebank

Kristopher Kyle, Hakyung Sung


Abstract
In this paper we introduce a freely available treebank that includes argument structure construction (ASC) annotation. We then use the treebank to train probabilistic annotation models that rely on verb lemmas and/ or syntactic frames. We also use the treebank data to train a highly accurate transformer-based annotation model (F1 = 91.8%). Future directions for the development of the treebank and annotation models are discussed.
Anthology ID:
2023.cxgsnlp-1.7
Original:
2023.cxgsnlp-1.7v1
Version 2:
2023.cxgsnlp-1.7v2
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:
51–62
Language:
URL:
https://aclanthology.org/2023.cxgsnlp-1.7
DOI:
Bibkey:
Cite (ACL):
Kristopher Kyle and Hakyung Sung. 2023. An Argument Structure Construction Treebank. In Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023), pages 51–62, Washington, D.C.. Association for Computational Linguistics.
Cite (Informal):
An Argument Structure Construction Treebank (Kyle & Sung, CxGsNLP-SyntaxFest 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.cxgsnlp-1.7.pdf
Video:
 https://aclanthology.org/2023.cxgsnlp-1.7.mov