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 CON-TAINS TLINK task, and the DCTR task.
Beyond the challenges, REF propose a model based on a structured perceptron to jointly predict both types of temporal relations.
Structured Learning for Temporal Relation Extraction from Clinical Records
EACL
Computer Science