A rule based solution to co-reference resolution in clinical text.
OBJECTIVE
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.
Department of Computer and Mathematical Sciences, University of Houston-Downtown, Houston, Texas, USA.
Ping
P
Chen
David
D
Hinote
Guoqing
G
Chen
D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D009323:Natural Language Processing