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Conference Papers Year : 2017

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

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.

Dates and versions

cea-01841667 , version 1 (17-07-2018)

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Cite

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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