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.
Leeuwenberg and Moens REF use structured perceptron model that jointly learns the relations between events and the document-creation time and the relations between events and time expressions in the text.
Structured Learning for Temporal Relation Extraction from Clinical Records
EACL
Computer Science