Objective: To research computational methods for coreference resolution in the clinical narrative and build a system implementing the best methods.
Methods: The Ontology Development and Information Extraction corpus annotated for coreference relations consists of 7214 coreferential markables, forming 5992 pairs and 1304 chains. We trained classifiers with semantic, syntactic, and surface features pruned by feature selection. For the three system components--for the resolution of relative pronouns, personal pronouns, and noun phrases--we experimented with support vector machines with linear and radial basis function (RBF) kernels, decision trees, and perceptrons. Evaluation of algorithms and varied feature sets was performed using standard metrics.
Results: The best performing combination is support vector machines with an RBF kernel and all features (MUC score=0.352, B(3)=0.690, CEAF=0.486, BLANC=0.596) outperforming a traditional decision tree baseline.
Discussion: The application showed good performance similar to performance on general English text. The main error source was sentence distances exceeding a window of 10 sentences between markables. A possible solution to this problem is hinted at by the fact that coreferent markables sometimes occurred in predictable (although distant) note sections. Another system limitation is failure to fully utilize synonymy and ontological knowledge. Future work will investigate additional ways to incorporate syntactic features into the coreference problem.
Conclusion: We investigated computational methods for coreference resolution in the clinical narrative. The best methods are released as modules of the open source Clinical Text Analysis and Knowledge Extraction System and Ontology Development and Information Extraction platforms.