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Data & Knowledge Engineering
SemEval-2010 task 13: evaluating events, time expressions, and temporal relations (TempEval-2)
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SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
HeidelTime: High quality rule-based extraction and normalization of temporal expressions
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
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Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
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Journal of Biomedical Informatics
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Automatic translation of clinical researcher data requests to executable database queries is instrumental to an effective interface between clinical researchers and "Big Clinical Data". A necessary step towards this goal is to parse ample temporal expressions in free-text researcher requests. This paper reports a novel algorithm called TEXer. It uses heuristic rule and pattern learning for extracting and normalizing temporal expressions in researcher requests. Based on 400 real clinical queries with human annotations, we compared our method with four baseline methods. TEXer achieved a precision of 0.945 and a recall of 0.858, outperforming all the baseline methods. We conclude that TEXer is an effective method for temporal expression extraction from free-text clinical data requests.