%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.