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| Framework for Document-level Event Extraction Based on Semantic Graph Prompt Learning |
| Yu Chuanming, Cheng Wei |
| School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073 |
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Abstract To address the issue that most existing models focus on sentence-level semantic abstraction and thus fail to fully exploit the advantages of abstract meaning representation (AMR) semantic structure information and semantic dependency relationships, this study proposes a document-level event extraction framework based on semantic graph prompts (SeGPL). By introducing a multigranularity AMR fusion mechanism and adaptive graph prompt features, the semantic modeling ability of the model was significantly improved. Compared to the strongest baseline TSAR, SeGPL achieved improvements of 2.16 and 3.36 percentage points in the head F1 scores for argument identification and classification on the WikiEvents dataset, respectively; on the RAMS dataset, it demonstrated increases of 0.41 and 1.13 percentage points in span F1 and head F1 scores, respectively, providing insights into event information extraction in long texts.
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Received: 11 March 2025
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