Algorithms for analysing the temporal structure of discourse
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Machine learning of temporal relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Classifying temporal relations between events
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Jointly combining implicit constraints improves temporal ordering
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
LCC-TE: a hybrid approach to temporal relation identification in news text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
WVALI: temporal relation identification by syntactico-semantic analysis
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TIPSem (English and Spanish): Evaluating CRFs and semantic roles in TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
NCSU: Modeling temporal relations with Markov logic and lexical ontology
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
Journal of Biomedical Informatics
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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.