@inproceedings{kyle-sung-2023-argument,
title = "An Argument Structure Construction Treebank",
author = "Kyle, Kristopher and
Sung, Hakyung",
editor = "Bonial, Claire and
Tayyar Madabushi, Harish",
booktitle = "Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)",
month = mar,
year = "2023",
address = "Washington, D.C.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.cxgsnlp-1.7",
pages = "51--62",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T An Argument Structure Construction Treebank
%A Kyle, Kristopher
%A Sung, Hakyung
%Y Bonial, Claire
%Y Tayyar Madabushi, Harish
%S Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)
%D 2023
%8 March
%I Association for Computational Linguistics
%C Washington, D.C.
%F kyle-sung-2023-argument
%X 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.
%U https://aclanthology.org/2023.cxgsnlp-1.7
%P 51-62
Markdown (Informal)
[An Argument Structure Construction Treebank](https://aclanthology.org/2023.cxgsnlp-1.7) (Kyle & Sung, CxGsNLP-SyntaxFest 2023)
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.