Structured Learning for Temporal Relation Extraction from Clinical Records

EACL 2017 Artuur LeeuwenbergMarie-Francine Moens

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... (read more)

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