Improving Event Detection via Trigger Word Expansion

Event detection is a fundamental task in information extraction, aiming to identify trigger words from given texts and categorize them into distinct event types.Existing approaches for event detection predominantly rely on annotating trigger words to classify events, which can lead to semantic recognition errors due to the oversimplified semantics of these trigger words.To tackle this challenge, we propose a trigger word expansionbased approach for robust contextual event detection.Specifically, we propose a framework comprising a trigger word extractor within a model for trigger word expansion classifier.This enables event detection to be conducted through trigger word expansion, enriching the semantics of trigger words.Additionally, we leverage the GPT model to generate contexts for the expanded trigger words, thereby enhancing the contextual understanding of trigger words.Extensive experiments conducted on the standard MAVEN benchmark dataset showcase the superior performance of our approach compared to state-of-the-art methods.This confirms the effectiveness of our proposed approach over existing approaches that rely on trigger word annotation.