A Joint Model for Entity Analysis: Coreference, Typing, and Linking

We present a joint model of three core tasks in the entity analysis stack: coreference res-olution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia en-tities). Our model is formally a structured con-ditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines.