A rule based solution to co-reference resolution in clinical text |
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Authors: | Ping Chen David Hinote Guoqing Chen |
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Institution: | 1.Department of Computer and Mathematical Sciences, University of Houston–Downtown, Houston, Texas, USA;2.VA HSR&D Center of Excellence (152), Baylor College of Medicine, Houston, Texas, USA |
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Abstract: | ObjectiveTo build an effective co-reference resolution system tailored to the biomedical domain.MethodsExperimental 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.ResultsThe 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.ConclusionsManually 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. |
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Keywords: | Co-reference Resolution Computational Linguistics Rule-based system Natural Language Processing |
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