Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
Table 4: Within-document test set results on ECB+. Note that Lemma is equivalent to Lemma-δ in the within-document setting. Cybulska and Vossen Cybulska and Vossen (2015) did not report the performance of their model in this setting.
['[BOLD] Model', 'R', 'MUC P', 'F', 'R', 'B3 P', 'F', 'CM F', 'R', 'CE P', 'F', 'R', 'BLANC P', 'F', 'CoNLL F']
These results are obtained by cutting all links drawn across documents for the gold standard chains and the predicted chains. We observe that, across all models, scores on the mention- and entity-based measures are substantially higher than the link-based measures (e.g., MUC and BLANC). The usefulness of CORE+CCE+Lemma (which initializes the clustering with the lemma-δ predictions and then continues to cluster with CORE+CCE) is exemplified by the improvements or matches in every measure when compared to both Lemma-δ and CORE+CCE. The most vivid improvement here is observed with the 10 point improvement in MUC over both models as well as the 4 and 5 point improvements in BLANC respectively, where the higher recall entails that CORE+CCE+Lemma confidently predicts coreference links that would otherwise have been false negatives.