EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Journal of Biomedical Informatics
On the value of temporal information in information retrieval
ACM SIGIR Forum
The DANTE Temporal Expression Tagger
Human Language Technology. Challenges of the Information Society
Intelligent visualization and exploration of time-oriented data of multiple patients
Artificial Intelligence in Medicine
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Journal of Biomedical Informatics
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Temporal information extraction from clinical narratives is of critical importance to many clinical applications. We participated in the EVENT/TIMEX3 track of the 2012 i2b2 clinical temporal relations challenge, and presented our temporal information extraction system, MedTime. MedTime comprises a cascade of rule-based and machine-learning pattern recognition procedures. It achieved a micro-averaged f-measure of 0.88 in both the recognitions of clinical events and temporal expressions. We proposed and evaluated three time normalization strategies to normalize relative time expressions in clinical texts. The accuracy was 0.68 in normalizing temporal expressions of dates, times, durations, and frequencies. This study demonstrates and evaluates the integration of rule-based and machine-learning-based approaches for high performance temporal information extraction from clinical narratives.