Structured Learning for Temporal Relation Extraction from Clinical Records

Artuur Leeuwenberg, Marie-Francine Moens


Abstract
We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We employ a structured perceptron, together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level. Moreover, this study gives insights in the results of integrating constraints for temporal relation extraction when using structured learning and prediction. Our best system outperforms the state-of-the art on both the CONTAINS TLINK task, and the DCTR task.
Anthology ID:
E17-1108
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1150–1158
Language:
URL:
https://www.aclweb.org/anthology/E17-1108
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1108.pdf