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- Publisher
- Oxford University Press
- Copyright
- Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions
- ISSN
- 1067-5027
- eISSN
- 1527-974X
- DOI
- 10.1136/amiajnl-2011-000770
- pmid
- 23059732
- Publisher site
- See Article on Publisher Site
Abstract
AbstractObjective To build an effective co-reference resolution system tailored to the biomedical domain.Methods Experimental materials used in this study were provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves co-reference resolution in medical documents. Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains. Normally, there are two ways of constructing a system to automatically discoverco-referent links. One is to manually build rules forco-reference resolution; the other is to use machine learning systems to learn automatically from training datasets and then perform the resolution task on testing datasets.Results The existing co-reference resolution systems are able to find some of the co-referent links; our rule based system performs well, finding the majority of the co-referent links. Our system achieved 89.6% overall performance on multiple medical datasets.Conclusions Manually crafted rules based on observation of training data is a valid way to accomplish high performance in this co-reference resolution task for the critical biomedical domain.
Journal
Journal of the American Medical Informatics Association – Oxford University Press
Published: Sep 11, 2013