Extracting Temporal Information from Open Domain Text: A Comparative Exploration

The utility of data-driven techniques in the end-to-end problem of temporal information extraction is unclear. Recognition of temporal expressions yields readily to machine learning, but normalization seems to call for a rule-based approach. We explore two aspects of the (potential) utility of data-driven methods in the temporal information extraction task. First, we look at whether improving recognition beyond the rule base used by a normalizer has an effect on normalization performance, comparing normalizer performance when fed by several recognition systems. We also perform an error analysis of our normalizer's performance to uncover aspects of the normalization task that might be amenable to data-driven techniques.