%0 Generic %A Tissot, H %A Gorrell, G %A Roberts, A %A Derczynski, L %A Fabro, MDD %C Denver, Colorado %D 2015 %E Post, Matt %E Kan, Min-Yen %E Bird, Steven %F discovery:10065714 %I Association for Computational Linguistics %P 835-839 %T UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval %U https://discovery.ucl.ac.uk/id/eprint/10065714/ %V 9 %X 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. %Z Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.