Towards a general theory of action and time
Artificial Intelligence
A maximum entropy approach to natural language processing
Computational Linguistics
From temporal expressions to temporal information: semantic tagging of news messages
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
Assigning time-stamps to event-clauses
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
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
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
CU-TMP: temporal relation classification using syntactic and semantic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Learning sentence-internal temporal relations
Journal of Artificial Intelligence Research
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Jointly identifying temporal relations with Markov Logic
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Global path-based refinement of noisy graphs applied to verb semantics
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Journal of Biomedical Informatics
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Patient discharge summaries provide detailed medical information about individuals who have been hospitalized. To make a precise and legitimate assessment of the abundant data, a proper time layout of the sequence of relevant events should be compiled and used to drive a patient-specific timeline, which could further assist medical personnel in making clinical decisions. The process of identifying the chronological order of entities is called temporal relation extraction. In this paper, we propose a hybrid method to identify appropriate temporal links between a pair of entities. The method combines two approaches: one is rule-based and the other is based on the maximum entropy model. We develop an integration algorithm to fuse the results of the two approaches. All rules and the integration algorithm are formally stated so that one can easily reproduce the system and results. To optimize the system's configuration, we used the 2012 i2b2 challenge TLINK track dataset and applied threefold cross validation to the training set. Then, we evaluated its performance on the training and test datasets. The experiment results show that the proposed TEMPTING (TEMPoral relaTion extractING) system (ranked seventh) achieved an F-score of 0.563, which was at least 30% better than that of the baseline system, which randomly selects TLINK candidates from all pairs and assigns the TLINK types. The TEMPTING system using the hybrid method also outperformed the stage-based TEMPTING system. Its F-scores were 3.51% and 0.97% better than those of the stage-based system on the training set and test set, respectively.