Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers
Julien Tourille, Olivier Ferret, Aurélie Névéol, Xavier Tannier
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.- Anthology ID:
- P17-2035
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 224–230
- Language:
- URL:
- https://aclanthology.org/P17-2035
- DOI:
- 10.18653/v1/P17-2035
- Cite (ACL):
- Julien Tourille, Olivier Ferret, Aurélie Névéol, and Xavier Tannier. 2017. Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 224–230, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers (Tourille et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/P17-2035.pdf