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:
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.cxgsnlp-1.7.pdf