title: Classifying temporal relations in clinical data: a hybrid, knowledge-rich approach.
abstract: We address the TLINK track of the 2012 i2b2 challenge on temporal relations. Unlike other approaches to this task, we (1) employ sophisticated linguistic knowledge derived from semantic and discourse relations, rather than focus on morpho-syntactic knowledge; and (2) leverage a novel combination of rule-based and learning-based approaches, rather than rely solely on one or the other. Experiments show that our knowledge-rich, hybrid approach yields an F-score of 69.3, which is the best result reported to date on this dataset.
authors: Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75080, USA. Electronic address: jennifer.l.dsouza@utdallas.edu.
authors: Jennifer
authors: J
authors: D'Souza
authors: Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75080, USA.
authors: Vincent
authors: V
authors: Ng
mesh_terms: D057286:Electronic Health Records; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics; D013997:Time Factors
keywords: Discourse relations; Hybrid approaches; Semantic relations; Temporal relations
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