We present two approaches to time expression identification, as entered in to SemEval2015 Task 6, Clinical TempEval. The first
is a comprehensive rule-based approach that
favoured recall, and which achieved the best
recall for time expression identification in Clinical TempEval. The second is an SVM-based
system built using readily available components, which was able to achieve a competitive F1 in a short development time. We discuss how the two approaches perform relative
to each other, and how characteristics of the
corpus affect the suitability of different approaches and their outcomes
