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.

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 .

Reconcile is one of the best implementations of the mention-pair model (Soon et al., 2001) of coreference resolution .

We show results on three popular and comparatively larger coreference resolution data sets — the ACE04, ACE05, and ACE05-ALL datasets from the ACE Program (NIST, 2004).

Many of the most difficult ambiguities in coreference resolution are semantic in nature.

There have been multiple previous systems that incorporate some form of world knowledge in coreference resolution tasks.

There is also work on end-to-end coreference resolution that uses large noun-similarity lists (Daumé III and Marcu, 2005) or structured knowledge bases such as Wikipedia (Yang and Su, 2007; Haghighi and Klein, 2009; Kobdani et al., 2011) and YAGO (Rahman and Ng, 2011).

Our Web features for coreference resolution are simple and capture a range of diffuse world knowledge.

datasets for end-to-end coreference resolution (see Section 4.3).

This keeps the total number of features small, which is important for the relatively small datasets used for coreference resolution .