W06-1623 Segmentation Our approach for temporal segmentation requires annotated data for supervised
W06-1623 Evaluation Measures We evaluate temporal segmentation by considering the ratio of correctly
W06-1623 hypothesis about the reliability of temporal segmentation . Once we established high inter-annotator
W06-1623 segmentation . 7 Results We evaluate temporal segmentation using leaveone-out cross-validation
W06-1623 boundary . Topical Continuity Temporal segmentation is closely related to topical
W06-1623 other diseases ) . 6.2 Annotating Temporal Segmentation Our approach for temporal segmentation
W06-1623 such transitions is relevant for temporal segmentation . We quantify the strength of
W06-1623 et al. , 2003 ) . 4 Method for Temporal Segmentation Our first goal is to automatically
W06-1623 method achieves 83 % F-measure in temporal segmentation and 84 % accuracy in inferring
W06-1623 learn them from data . We model temporal segmentation as a binary classification task
W11-4208 research tasks related to its temporal segmentation . 3.2 Methods We have developed
E12-2016 integration within a framework for temporal segmentation of the Patient history into episodes
W11-4200 ´ el ` ene Metzger Towards Temporal Segmentation of Patient History in Discharge
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