Abstract
In this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for event and temporal expression detection, and a structured perceptron for extracting temporal relations. Moreover, we present and analyze the results from our submissions, and verify the effectiveness of several system components. Our system performed above average for all subtasks in both phases.- Anthology ID:
- S17-2181
- 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:
- 1030–1034
- Language:
- URL:
- https://aclanthology.org/S17-2181
- DOI:
- 10.18653/v1/S17-2181
- Cite (ACL):
- Artuur Leeuwenberg and Marie-Francine Moens. 2017. KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 1030–1034, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records (Leeuwenberg & Moens, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/S17-2181.pdf
- Code
- tuur/ClinicalTempEval2017