@inproceedings{miller-etal-2023-end,
title = "End-to-end clinical temporal information extraction with multi-head attention",
author = "Miller, Timothy and
Bethard, Steven and
Dligach, Dmitriy and
Savova, Guergana",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.28",
doi = "10.18653/v1/2023.bionlp-1.28",
pages = "313--319",
abstract = "Understanding temporal relationships in text from electronic health records can be valuable for many important downstream clinical applications. Since Clinical TempEval 2017, there has been little work on end-to-end systems for temporal relation extraction, with most work focused on the setting where gold standard events and time expressions are given. In this work, we make use of a novel multi-headed attention mechanism on top of a pre-trained transformer encoder to allow the learning process to attend to multiple aspects of the contextualized embeddings. Our system achieves state of the art results on the THYME corpus by a wide margin, in both the in-domain and cross-domain settings.",
}
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<abstract>Understanding temporal relationships in text from electronic health records can be valuable for many important downstream clinical applications. Since Clinical TempEval 2017, there has been little work on end-to-end systems for temporal relation extraction, with most work focused on the setting where gold standard events and time expressions are given. In this work, we make use of a novel multi-headed attention mechanism on top of a pre-trained transformer encoder to allow the learning process to attend to multiple aspects of the contextualized embeddings. Our system achieves state of the art results on the THYME corpus by a wide margin, in both the in-domain and cross-domain settings.</abstract>
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%0 Conference Proceedings
%T End-to-end clinical temporal information extraction with multi-head attention
%A Miller, Timothy
%A Bethard, Steven
%A Dligach, Dmitriy
%A Savova, Guergana
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F miller-etal-2023-end
%X Understanding temporal relationships in text from electronic health records can be valuable for many important downstream clinical applications. Since Clinical TempEval 2017, there has been little work on end-to-end systems for temporal relation extraction, with most work focused on the setting where gold standard events and time expressions are given. In this work, we make use of a novel multi-headed attention mechanism on top of a pre-trained transformer encoder to allow the learning process to attend to multiple aspects of the contextualized embeddings. Our system achieves state of the art results on the THYME corpus by a wide margin, in both the in-domain and cross-domain settings.
%R 10.18653/v1/2023.bionlp-1.28
%U https://aclanthology.org/2023.bionlp-1.28
%U https://doi.org/10.18653/v1/2023.bionlp-1.28
%P 313-319
Markdown (Informal)
[End-to-end clinical temporal information extraction with multi-head attention](https://aclanthology.org/2023.bionlp-1.28) (Miller et al., BioNLP 2023)
ACL