End-to-end coreference resolution for clinical narratives

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 important domain of clinical text. Clinical text is unique because it requires significant use of domain knowledge to support coreference resolution. It also has specific discourse characteristics which impose several constraints on coreference decisions. We present a principled framework to incorporate knowledge-based constraints in the coreference model. We also show that different pronouns behave quite differently, necessitating the development of distinct ways for resolving different pronouns. Our methods result in significant performance improvements and we report the best results on a clinical corpora that has been used in coreference shared tasks. Moreover, for the first time, we report the results for end-to-end coreference resolution on this corpora.