Temporal Relation Identification and Classification in Clinical Notes
Jennifer D’Souza, Vincent Ng · 2013
We examine the task of temporal relation classification for the clinical domain. Our approach to this task departs from existing ones in that it is (1) knowledge-rich, employing sophisticated knowledge derived from semantic and discourse relations, and (2) hybrid, combining the strengths of rule-based and learning-based approaches. Evaluation results on the i2b2 Clinical Temporal Relations Challenge corpus show that our approach yields a 15--21% and 6--13% relative reduction in error over a state-of-the-art learning-based baseline system when gold-standard and automatically identified temporal relations are used, respectively.