Using Knowledge and Constraints To Find the Best Antecedent
Prateek Jindal, Dan Roth · 2012
Coreference resolution is the problem of clustering mentions into entities and is very critical for natural language understanding. This paper studies the problem of coreference resolution in the context of the newly emerging domain of Electronic Health Records (EHRs). The commonly used “best-link ” model for coreference resolution considers only the scores from a pairwise classifier in selecting the best antecedent. In this paper, we extend this model to include several constraints derived from surface-form of the mentions and the context in which they appear. Another major contribution of this paper is to show the use of domain-specific knowledge sources, mention parsing and clinical descriptors in deriving features which contribute to improved coreference resolution performance. We present experiments on 4 different clinical datasets illustrating that our approach outperforms a strong baseline and a state-of-the-art system by a wide margin.