@inproceedings{yao-etal-2023-textual,
title = "Textual Entailment for Temporal Dependency Graph Parsing",
author = "Yao, Jiarui and
Bethard, Steven and
Wright-Bettner, Kristin and
Goldner, Eli and
Harris, David and
Savova, Guergana",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.25",
doi = "10.18653/v1/2023.clinicalnlp-1.25",
pages = "191--199",
abstract = "We explore temporal dependency graph (TDG) parsing in the clinical domain. We leverage existing annotations on the THYME dataset to semi-automatically construct a TDG corpus. Then we propose a new natural language inference (NLI) approach to TDG parsing, and evaluate it both on general domain TDGs from wikinews and the newly constructed clinical TDG corpus. We achieve competitive performance on general domain TDGs with a much simpler model than prior work. On the clinical TDGs, our method establishes the first result of TDG parsing on clinical data with 0.79/0.88 micro/macro F1.",
}
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<abstract>We explore temporal dependency graph (TDG) parsing in the clinical domain. We leverage existing annotations on the THYME dataset to semi-automatically construct a TDG corpus. Then we propose a new natural language inference (NLI) approach to TDG parsing, and evaluate it both on general domain TDGs from wikinews and the newly constructed clinical TDG corpus. We achieve competitive performance on general domain TDGs with a much simpler model than prior work. On the clinical TDGs, our method establishes the first result of TDG parsing on clinical data with 0.79/0.88 micro/macro F1.</abstract>
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%0 Conference Proceedings
%T Textual Entailment for Temporal Dependency Graph Parsing
%A Yao, Jiarui
%A Bethard, Steven
%A Wright-Bettner, Kristin
%A Goldner, Eli
%A Harris, David
%A Savova, Guergana
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yao-etal-2023-textual
%X We explore temporal dependency graph (TDG) parsing in the clinical domain. We leverage existing annotations on the THYME dataset to semi-automatically construct a TDG corpus. Then we propose a new natural language inference (NLI) approach to TDG parsing, and evaluate it both on general domain TDGs from wikinews and the newly constructed clinical TDG corpus. We achieve competitive performance on general domain TDGs with a much simpler model than prior work. On the clinical TDGs, our method establishes the first result of TDG parsing on clinical data with 0.79/0.88 micro/macro F1.
%R 10.18653/v1/2023.clinicalnlp-1.25
%U https://aclanthology.org/2023.clinicalnlp-1.25
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.25
%P 191-199
Markdown (Informal)
[Textual Entailment for Temporal Dependency Graph Parsing](https://aclanthology.org/2023.clinicalnlp-1.25) (Yao et al., ClinicalNLP 2023)
ACL
- Jiarui Yao, Steven Bethard, Kristin Wright-Bettner, Eli Goldner, David Harris, and Guergana Savova. 2023. Textual Entailment for Temporal Dependency Graph Parsing. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 191–199, Toronto, Canada. Association for Computational Linguistics.