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Neural architecture for temporal relation extraction: A Bi-LSTM approach for detecting narrative containers

J. Tourille 1 Olivier Ferret 2 X. Tannier 1 Aurélie Névéol 1 
1 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
2 DIASI - Département Intelligence Ambiante et Systèmes Interactifs
Abstract : We present a neural architecture for containment relation identification between medical events and/or temporal expressions. We experiment on a corpus of de-identified clinical notes in English from the Mayo Clinic, namely the THYME corpus. Our model achieves an F-measure of 0.613 and outperforms the best result reported on this corpus to date.
Keywords : Clinical notes Computational linguistics F measure Neural architectures Temporal relation Temporal expressions Linguistics
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Conference papers
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https://hal-cea.archives-ouvertes.fr/cea-01841667
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Tuesday, July 17, 2018 - 2:35:28 PM
Last modification on : Saturday, June 25, 2022 - 10:32:42 PM

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CEA | CNRS | LIMSI | DRT | CEA-UPSAY | UNIV-PARIS-SACLAY | LIST | SORBONNE-UNIVERSITE | SU-TI | ANR | LISN | GS-ENGINEERING | GS-COMPUTER-SCIENCE

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J. Tourille, Olivier Ferret, X. Tannier, Aurélie Névéol. Neural architecture for temporal relation extraction: A Bi-LSTM approach for detecting narrative containers. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Jul 2017, Vancouver, Canada. pp.224-230, ⟨10.18653/v1/P17-2035⟩. ⟨cea-01841667⟩

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