@inproceedings{cai-etal-2024-adapting,
title = "Adapting {A}bstract {M}eaning {R}epresentation Parsing to the Clinical Narrative {--} the {SPRING} {THYME} parser",
author = "Cai, Jon and
Wright-Bettner, Kristin and
Palmer, Martha and
Savova, Guergana and
Martin, James",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Bitterman, Danielle",
booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clinicalnlp-1.23",
doi = "10.18653/v1/2024.clinicalnlp-1.23",
pages = "271--282",
abstract = "This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, thereby enhancing the interpretability and usability of clinical text data at scale. Leveraging the colon cancer dataset from the Temporal Histories of Your Medical Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing continuous training. Our approach incorporates data augmentation techniques to enhance the accuracy of AMR structure predictions. Notably, through this learning strategy, our parser achieved an impressive F1 score of 88{\%} on the THYME corpus{'}s colon cancer dataset. Moreover, our research delved into the efficacy of data required for domain adaptation within the realm of clinical notes, presenting domain adaptation data requirements for AMR parsing. This exploration not only underscores the parser{'}s robust performance but also highlights its potential in facilitating a deeper understanding of clinical narratives through structured semantic representations.",
}
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<abstract>This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, thereby enhancing the interpretability and usability of clinical text data at scale. Leveraging the colon cancer dataset from the Temporal Histories of Your Medical Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing continuous training. Our approach incorporates data augmentation techniques to enhance the accuracy of AMR structure predictions. Notably, through this learning strategy, our parser achieved an impressive F1 score of 88% on the THYME corpus’s colon cancer dataset. Moreover, our research delved into the efficacy of data required for domain adaptation within the realm of clinical notes, presenting domain adaptation data requirements for AMR parsing. This exploration not only underscores the parser’s robust performance but also highlights its potential in facilitating a deeper understanding of clinical narratives through structured semantic representations.</abstract>
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%0 Conference Proceedings
%T Adapting Abstract Meaning Representation Parsing to the Clinical Narrative – the SPRING THYME parser
%A Cai, Jon
%A Wright-Bettner, Kristin
%A Palmer, Martha
%A Savova, Guergana
%A Martin, James
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Bitterman, Danielle
%S Proceedings of the 6th Clinical Natural Language Processing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F cai-etal-2024-adapting
%X This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, thereby enhancing the interpretability and usability of clinical text data at scale. Leveraging the colon cancer dataset from the Temporal Histories of Your Medical Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing continuous training. Our approach incorporates data augmentation techniques to enhance the accuracy of AMR structure predictions. Notably, through this learning strategy, our parser achieved an impressive F1 score of 88% on the THYME corpus’s colon cancer dataset. Moreover, our research delved into the efficacy of data required for domain adaptation within the realm of clinical notes, presenting domain adaptation data requirements for AMR parsing. This exploration not only underscores the parser’s robust performance but also highlights its potential in facilitating a deeper understanding of clinical narratives through structured semantic representations.
%R 10.18653/v1/2024.clinicalnlp-1.23
%U https://aclanthology.org/2024.clinicalnlp-1.23
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.23
%P 271-282
Markdown (Informal)
[Adapting Abstract Meaning Representation Parsing to the Clinical Narrative – the SPRING THYME parser](https://aclanthology.org/2024.clinicalnlp-1.23) (Cai et al., ClinicalNLP-WS 2024)
ACL