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UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval

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. Green open access

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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-pp.ucl.ac.uk/id/eprint/10065714
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