CONE: Metrics for Automatic Evaluation of Named Entity Co-Reference Resolution
Bo Lin, Rushin Shah, Robert Frederking, Anatole V. Gershman · 2010
Human annotation for Co-reference Resolution (CRR) is labor intensive and costly, and only a handful of annotated corpora are currently available. However, corpora with Named Entity (NE) annotations are widely available. Also, unlike current CRR systems, state-of-the-art NER systems have very high accuracy and can generate NE labels that are very close to the gold standard for unlabeled corpora. We propose a new set of metrics collectively called CONE for Named Entity Coreference Resolution (NE-CRR) that use a subset of gold standard annotations, with the advantage that this subset can be easily approximated using NE labels when gold standard CRR annotations are absent. We define CONE B 3 and CONE CEAF metrics based on the traditional B 3 and CEAF metrics and show that CONE B 3 and CONE CEAF scores of any CRR system on any dataset are highly correlated with its B 3 and CEAF scores respectively. We obtain correlation factors greater than 0.6 for all CRR systems across all datasets, and a best-case correlation factor of 0.8. We also present a baseline method to estimate the gold standard required by CONE metrics, and show that CONE B 3 and CONE CEAF scores using this estimated gold standard are also correlated with B 3 and CEAF scores respectively. We thus demonstrate the suitability of CONE B 3 and CONE CEAF for automatic evaluation of NE-CRR. 1