@inproceedings{discovery10065714,
         journal = {Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)},
       publisher = {Association for Computational Linguistics},
            year = {2015},
           title = {UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval},
          series = {International Workshop on Semantic Evaluation (SemEval 2015)},
           month = {June},
          editor = {Matt Post and Min-Yen Kan and Steven Bird},
           pages = {835--839},
       booktitle = {Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)},
         address = {Denver, Colorado},
            note = {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.},
          volume = {9},
             url = {https://doi.org/10.18653/v1/S15-2141},
          author = {Tissot, H and Gorrell, G and Roberts, A and Derczynski, L and Fabro, MDD},
        abstract = {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.}
}