Abstract
Temporality is crucial in understanding the course of clinical events from a patient’s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.- Anthology ID:
- S17-2178
- Original:
- S17-2178v1
- Version 2:
- S17-2178v2
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1014–1018
- Language:
- URL:
- https://aclanthology.org/S17-2178
- DOI:
- 10.18653/v1/S17-2178
- Cite (ACL):
- Yu Long, Zhijing Li, Xuan Wang, and Chen Li. 2017. XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 1014–1018, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model (Long et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/S17-2178.pdf