Extracting Time Expressions from Clinical Text

Temporal information extraction is important to understanding text in clinical documents. Temporal expression extraction provides explicit grounding of events in a narrative. In this work we provide a direct comparison of various ways of extracting temporal expressions, using similar features as much as possible to explore the advantages of the methods themselves. We evaluate these systems on both the THYME (Temporal History of Your Medical Events) and i2b2 Challenge corpora. Our main findings are that simple sequence taggers outperform conditional random fields on the new data, and higher-level syntactic features do not seem to improve performance.

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