To address semantic ambiguities in coreference resolution, we use Web n-gram features that capture a range of world knowledge in a diffuse but robust way. Specifically, we exploit short-distance cues to hypernymy, semantic compatibility, and semantic context, as well as general lexical co-occurrence. When added to a state-of-the-art coreference baseline, our Web features give significant gains on multiple datasets (ACE 2004 and ACE 2005) and metrics (MUC and B3), resulting in the best results reported to date for the end-to-end task of coreference resolution.
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