Abstract
Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text. We present a system for this task that uses supervised learning for the extraction of temporal expression and event spans with corresponding attributes and narrative container relations. Approaches include conditional random fields and decision tree ensembles, using lexical, syntactic, semantic, distributional, and rule-based features. Our system received best or second best scores in TIMEX3 span, EVENT span, and CONTAINS relation extraction.- Anthology ID:
- S17-2180
- 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:
- 1024–1029
- Language:
- URL:
- https://aclanthology.org/S17-2180
- DOI:
- 10.18653/v1/S17-2180
- Cite (ACL):
- Sean MacAvaney, Arman Cohan, and Nazli Goharian. 2017. GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 1024–1029, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction (MacAvaney et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/S17-2180.pdf