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The document discusses generating date properties from a given message date and using machine learning to predict dates from natural language expressions. It proposes using features from text near an extracted date expression and evaluating classifiers like Naive Bayes, decision trees, SVM and logistic regression to predict meeting times. Issues addressed include data sparsity, error propagation from upstream components, and fixing segmentation faults in the existing TEA time extraction system.
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