Revisiting Joint Modeling of Cross-documentEntity and Event Coreference Resolution
Table 3: Combined within- and cross-document event coreference results on the ECB+ test set.
['[BOLD] Model', 'R', 'MUC P', '[ITALIC] F1', 'R', 'B3 P', '[ITALIC] F1', 'R', 'CEAF- [ITALIC] e P', '[ITALIC] F1', 'CoNLL  [ITALIC] F1']
Table 3 presents the results on event coreference. Our joint model outperforms all the base [CONTINUE] lines with a gap of 10.5 CoNLL F1 points from the last published results (KCP), while surpassing our strong lemma baseline by 3 points. [CONTINUE] The results reconfirm that the lemma baseline, when combined with effective topic clustering, is a strong baseline for CD event coreference resolution on the ECB+ corpus (Upadhyay et al., 2016). [CONTINUE] The results of CLUSTER+KCP again indicate that pre-clustering of documents to topics is beneficial, improving upon the KCP performance by 4.6 points, though still performing substantially worse than our joint model. [CONTINUE] To test the contribution of joint modeling, we compare our joint model to its disjoint variant. We observe that the joint model performs better on both event and entity coreference. The performance gap is modest but significant with bootstrapping and permutation tests (p < 0.001).