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.

The mention-pair model relies on a pairwise function to determine whether or not two mentions are coreferent .

Pairwise predictions are then consolidated by transitive closure (or some other clustering method) to form the final set of coreference clusters (chains).

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

For resolving coreference in this example, a system would benefit from the world knowledge that Obama is the president.

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

Methods that measure compatibility between mention pairs are currently the dominant approach to coreference .

These trees succinctly summarize the mentions providing a highly compact, information-rich structure for reasoning about entities and coreference uncertainty at massive scales.

We demonstrate that the hierarchical model is several orders of magnitude faster than pairwise, allowing us to perform coreference on six million author mentions in under four hours on a single CPU.

Coreference resolution, the task of clustering mentions into partitions representing their underlying real-world entities, is fundamental for high-level information extraction and data integration, including semantic search, question answering, and knowledge base construction.

For example, coreference is Vital for determining author publication lists in bibliographic knowledge bases such as CiteSeer and Google Scholar, where the repository must know if the “R.

31 for Fast Coreference at Large Scale

0 Coreference : indicates that two chunks refer to

The processing includes dependency parsing, named entity recognition and coreference resolution, done with the Stanford CoreNLP software (Klein and Manning, 2003); and events and temporal information extraction, via the TARSQI Toolkit (Verhagen et al., 2005).

Each node of GO clusters together coreferent nodes, representing a discourse referent.

Therefore, besides canonicalizing named entities, the model also resolves within—document and cross-document coreference , since it assigned a row index for every mention.

As a result, the model discovers parts of names—(Mrs., Michelle, Obama)—while simultaneously performing coreference resolution for named entity mentions.

Our model is focused on the problem of canonicalizing mention strings into their parts, though its 7“ variables (which map mentions to rows) could be interpreted as (within-document and cross-document) coreference resolution, which has been tackled using a range of probabilistic models (Li et al., 2004; Haghighi and Klein, 2007; Poon and Domingos, 2008; Singh et al., 2011).