Overlapping event extraction aims to identify triggers, arguments, and semantic roles when multiple events share textual components within the same sentence. In agricultural text, this task is particularly challenging because shared arguments may play different roles across events, while compound terms and long-span expressions often exhibit ambiguous boundaries. Existing methods still suffer from insufficient event conditioning and weak exploitation of boundary cues, which easily lead to role confusion and noisy span candidates during decoding. To address these issues, we propose ESBR, a structured model that integrates event-conditioned encoding, structured span inference, hierarchical boundary reasoning, and boundary-guided decoding. We further enhance training and inference stability with uncertainty-aware weighting and adaptive thresholding. Experiments on the public benchmark FewFC and our constructed agricultural benchmark FewAgri show that ESBR consistently outperforms competitive baselines, with especially notable gains on argument identification and role classification.

Abstract Anonymous communication serves as the preferred tool for cyber attackers to evade detection, posing a serious threat to cyberspace security. Accurately tracking the attackers in anonymous communication is crucial for defending against attacks. Flow correlation is an effective method that can link flows in the anonymous network. Existing flow correlation methods usually rely on a long observation, resulting in reduced correlation precision and limited generalization ability within anonymous communication. To address this issue, we propose a robust two-stage flow correlation method called RTsFCM via Siamese network and ensemble voting scheme. In the first stage, a Siamese network with shared weights is utilized to automatically extract the multilevel features from ingress flow and egress flow, respectively. Further, they are concatenated to generate a more expressive feature set to enhance true positive rate (TPR). In the second stage, flow pairs are firstly divided into a series of partially overlapping sub-flows(windows) in view of flow duration. Then, pairwise comparison for each window is conducted independently and the ensemble voting scheme is adopted across these windows to reduce the false positive rate (FPR) significantly. Experimental results show that RTsFCM is superior to the state of the art, achieving over a 4% increase in both TPR and F1_score. Simultaneously, it obtains an FPR as low as 0.68%, utilizing the packet timing characteristics within the initial portion of a flow.