Abstract
BackgroundMost of the current work on clinical temporal relation identification follows the convention developed in the general domain, aiming to identify a comprehensive set of temporal relations from a document including both explicit and implicit relations. While such a comprehensive set can represent temporal information in a document in a complete manner, some of the temporal relations in the comprehensive set may not be essential depending on the clinical application of interest. Moreover, as the types of evidence that should be used to identify explicit and implicit relations are different, current clinical temporal relation identification systems that target both explicit and implicit relations still show low performances for practical use.MethodsIn this paper, we propose to focus on a sub-task of conventional temporal relation identification task in order to provide insight into building practical temporal relation identification modules for clinical text. We focus on identification of direct temporal relations, a subset of temporal relations that is chosen to minimize the amount of inference required to identify the relations. A corpus on direct temporal relations between time expressions and event mentions is constructed, and an automatic system tailored for direct temporal relations is developed.ResultsIt is shown that the direct temporal relations constitute a major category of temporal relations that contain important information needed for clinical applications. The system optimized for direct temporal relations achieves better performance than the state-of-the-art system developed with comprehensive set of both explicit and implicit relations in mind.ConclusionsWe expect direct temporal relations to facilitate the development of practical temporal information extraction tools in clinical domain.
Highlights
Most of the current work on clinical temporal relation identification follows the convention developed in the general domain, aiming to identify a comprehensive set of temporal relations from a document including both explicit and implicit relations
While the task of temporal information identification ranges from identifying time mentions from the text [11, 12] to answering time-related questions [1, 13], this paper focuses on temporal relation identification, which is an essential task in understanding temporality from clinical text
Much work has been done for temporal relation identification from clinical text, the state-of-the-art performance is still not adequate for wide adoption in practical applications; the best systems’ performances for recent challenges have F1-scores of 57.3 [9] and 69.43 [10], respectively
Summary
Most of the current work on clinical temporal relation identification follows the convention developed in the general domain, aiming to identify a comprehensive set of temporal relations from a document including both explicit and implicit relations. While such a comprehensive set can represent temporal information in a document in a complete manner, some of the temporal relations in the comprehensive set may not be essential depending on the clinical application of interest. The corpora include implicit temporal relations that are only identifiable through inference, combining multiple pieces of information, as well as temporal relations that are explicitly stated in the text. The transitive closure Of a given set of manually annotated temporal relations is calculated and used as the full set of temporal relations identifiable from a text
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