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Clinical reports such as progress notes, discharge summaries and pathology reports contain information essential for treatment planning, decision support, research and clinical audit. However, extracting explicit named entities such as people, symptoms, conditions, tests and treatments is insufficient for this purpose. Resolution of coreference - where two expressions refer to the same real-world entity or event - is required to uncover linked narrative events that are often implicit in the text.
This problem has only recently been considered for clinical notes, despite being a well-researched topic for general newswire and media texts. In this presentation, we consider what Huggy Bear and House can tell us about coreference relations that are easily expressed and understood by humans, but are difficult to process computationally.