@article{styler-iv-etal-2014-temporal,
title = "Temporal Annotation in the Clinical Domain",
author = "Styler IV, William F. and
Bethard, Steven and
Finan, Sean and
Palmer, Martha and
Pradhan, Sameer and
de Groen, Piet C and
Erickson, Brad and
Miller, Timothy and
Lin, Chen and
Savova, Guergana and
Pustejovsky, James",
editor = "Lin, Dekang and
Collins, Michael and
Lee, Lillian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "2",
year = "2014",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q14-1012",
doi = "10.1162/tacl_a_00172",
pages = "143--154",
abstract = "This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, {``}the THYME Guidelines to ISO-TimeML (THYME-TimeML){''}. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.",
}
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<abstract>This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.</abstract>
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%0 Journal Article
%T Temporal Annotation in the Clinical Domain
%A Styler IV, William F.
%A Bethard, Steven
%A Finan, Sean
%A Palmer, Martha
%A Pradhan, Sameer
%A de Groen, Piet C.
%A Erickson, Brad
%A Miller, Timothy
%A Lin, Chen
%A Savova, Guergana
%A Pustejovsky, James
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F styler-iv-etal-2014-temporal
%X This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.
%R 10.1162/tacl_a_00172
%U https://aclanthology.org/Q14-1012
%U https://doi.org/10.1162/tacl_a_00172
%P 143-154
Markdown (Informal)
[Temporal Annotation in the Clinical Domain](https://aclanthology.org/Q14-1012) (Styler IV et al., TACL 2014)
ACL
- William F. Styler IV, Steven Bethard, Sean Finan, Martha Palmer, Sameer Pradhan, Piet C de Groen, Brad Erickson, Timothy Miller, Chen Lin, Guergana Savova, and James Pustejovsky. 2014. Temporal Annotation in the Clinical Domain. Transactions of the Association for Computational Linguistics, 2:143–154.