title: Knowledge-rich temporal relation identification and classification in clinical notes.
abstract: 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 (i) 'knowledge-rich', employing sophisticated knowledge derived from discourse relations as well as both domain-independent and domain-dependent semantic relations, and (ii) '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 17-24% and 8-14% 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. Database URL: http://www.hlt.utdallas.edu/~jld082000/temporal-relations/
authors.affiliation: Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688 jld082000@utdallas.edu.
authors.forename: Jennifer
authors.initials: J
authors.lastname: D'Souza
authors.affiliation: Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688.
authors.forename: Vincent
authors.initials: V
authors.lastname: Ng
mesh_terms: D019992:Databases as Topic; D006801:Humans; D051188:Knowledge Bases; D008490:Medical Informatics; D012660:Semantics; D013997:Time Factors