Structured Learning for Temporal Relation Extraction from Clinical Records
TL
TL;DR
This study employs 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.
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.
References30 items
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Improving Temporal Relation Extraction with Training Instance Augmentation
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SemEval-2016 Task 12: Clinical TempEval
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KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records
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Markov Logic Networks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming
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