BALANCE-DEE: Balancing Event Candidate Space Expansion and False-Positive Correction for Document-Level Event Extraction

Document-level event extraction (DEE) systems often miss non-salient events and make cross-event argument assignment errors. Expanding the event candidate space can recover missed events, but it also introduces spurious candidates and increases structural conflicts. We study this coverage–correction tradeoff and propose BALANCE-DEE, a structured refinement framework that jointly evaluates candidate-event validity and state-conditioned action gain. A discriminatively enhanced schema guides a frozen large language model to propose potentially missed events; a two-factor eventness assessment based on event instantiation and textual support filters unsupported candidates; and a learned action-gain function scores insertion, deletion, and split-and-reallocation edits relative to the current event-record state. The model iteratively executes positive-gain edits and recomputes state-conditioned gains after each update. Across FNDEE, CrudeOilNews, and SENTiVENT, BALANCE-DEE improves trigger and argument F1 over the corresponding base extractor. On FNDEE, it raises overall event recall by 2.3 percentage points and non-salient event recall by 4.2 points, reduces over-extracted arguments by 13.2%, and lowers the cross-event argument attachment rate from 20.4% to 12.9%. These results show that high-recall candidate expansion is most effective when coupled with state-aware validation and structured correction.