Tissot, H;
Gorrell, G;
Roberts, A;
Derczynski, L;
Fabro, MDD;
(2015)
UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval.
In: Post, Matt and Kan, Min-Yen and Bird, Steven, (eds.)
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015).
(pp. pp. 835-839).
Association for Computational Linguistics: Denver, Colorado.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval |
| Event: | 9th International Workshop on Semantic Evaluation (SemEval 2015) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18653/v1/S15-2141 |
| Publisher version: | https://doi.org/10.18653/v1/S15-2141 |
| Language: | English |
| Additional information: | 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10065714 |
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