MCC-ED: a multi-module collaborative framework for event detection via contextual interaction, closed-loop graph fusion, and event contrastive learning

Abstract

Event detection (ED) is a fundamental task in textual information extraction and underpins a wide range of downstream applications, including public opinion monitoring, knowledge graph construction, and intelligent decision-making. Despite recent progress, existing ED models remain limited by insufficient contextual modeling, severe event-semantic ambiguity, and limited adaptability to heterogeneous texts in complex scenarios. To address these challenges, this paper proposes MCC-ED, a multi-module collaborative event detection framework. Built upon pretrained contextual representations, MCC-ED employs dataset-specific decoding heads, including a conditional random field (CRF) decoder for sequence labeling and a span prediction decoder for extractive question answering. The framework integrates three mutually reinforcing modules: a multi-layer fine-grained contextual interaction module that strengthens deep feature associations; a closed-loop self-evaluation multi-source graph fusion module that combines contextual semantics with valid-token sequence structure through iterative feedback; and an event contrastive learning module that constructs category-level contrastive samples to enlarge decision margins and mitigate semantic confusion. Experiments on ACE-2005 yield 86.76% F1 for Trigger Identification (Tri-ID) and 85.56% for Trigger Identification Classification (Tri-CLS), while MAVEN results reach 76.12% and 75.75%, respectively. These results demonstrate strong detection accuracy and consistent effectiveness across the two benchmark settings without conflating their task-specific decoding protocols.