Abstract
Clinical TempEval 2015 brought the temporal information extraction tasks of past TempEval campaigns to the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were trained and evaluated on a corpus of clinical notes and pathology reports from the Mayo Clinic, annotated with an extension of TimeML for the clinical domain. Three teams submitted a total of 13 system runs, with the best systems achieving near-human performance on identifying events and times, but with a large performance gap still remaining for temporal relations.
Leon Derczynski
Associate professor
My research interests include NLP for misinformation detection and verification, clinical record processing, online harms, and efficient AI.