Skip to Main content Skip to Navigation
Conference papers

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 : Computational linguistics Clinical notes F measure Neural architectures Temporal expressions Temporal relation Linguistics
Document type :
Conference papers
Complete list of metadatas
https://hal-cea.archives-ouvertes.fr/cea-01841667
Contributor : Léna Le Roy <>
Submitted on : Tuesday, July 17, 2018 - 2:35:28 PM
Last modification on : Wednesday, October 14, 2020 - 4:00:23 AM

Links full text

openaccess

Identifiers

Collections

CEA | CNRS | CEA-UPSAY | DRT | LIMSI | LIST | UNIV-PARIS-SACLAY | SORBONNE-UNIVERSITE

Citation

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⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

158