Abstract
We present two approaches to time expression identification, as entered in to SemEval-2015 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.
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CITATION STYLE
Tissot, H., Gorrell, G., Roberts, A., Derczynski, L., & Del Fabro, M. D. (2015). UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval. In SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 835–839). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2141
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