Learning Event-aware Measures for Event Coreference Resolution

Researchers are witnessing knowledge-inspired natural language processing shifts the focus from entity-level to event-level, whereas event coreference resolution is one of the core challenges. This paper proposes a novel model for within-document event coreference resolution. On the basis of event but not entity as before, our model learns and integrates multiple representations from both event alone and event pair. For the former, we introduce multiple linguistics-motivated event alone features for more discriminative event representations. For the latter, we consider multiple similarity measures to capture the distinctions of event pairs. Our proposed model achieves new state-of-theart on the ACE 2005 benchmark, demonstrating the effectiveness of our proposed framework.