End-to-End Coreference Resolution via Hypergraph Partitioning

We describe a novel approach to coreference resolution which implements a global decision via hypergraph partitioning. In constrast to almost all previous approaches, we do not rely on separate classification and clustering steps, but perform coreference resolution globally in one step. Our hypergraph-based global model implemented within an end-to-end coreference resolution system outperforms two strong baselines (Soon et al., 2001; Bengtson & Roth, 2008) using system mentions only.

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