Enhancing Event Candidate Acquisition for Event Linking

2026-09-11

AI Summary

为解决事件链接中的候选事件获取问题，提出MACE方法，利用专门化LLM代理收集证据并优化事件结构，实验表明该方法能提高链接准确性。

Abstract

Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods use different architectures, candidate event acquisition can still be weakened by short ambiguous mentions, noisy arguments, and evidence that is unevenly useful for retrieval. We present MACE, a Multi-Agent Candidate Event acquisition method that refines event structure before linking. MACE uses evidence-specialized LLM agents to acquire time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and lets a coordinator revise the evidence set before final candidate construction. Experiments on two event linking benchmarks show that adding MACE to different event linking models consistently improves accuracy. These results show that MACE improves event linking through better candidate event acquisition without modifying the event linking model.

Problem

• Event Linking
• Candidate Acquisition
• Ambiguous Mentions
• Noisy Arguments
• Unevenly Useful Evidence

Innovation

• Multi-Agent Candidate Event
• evidence-specialized LLM agents
• intermediate queries
• candidate-event lookup tools
• event linking