Knowledge Augmented Entity and Relation Extraction for Legal Documents with Hypergraph Neural Network

• URL: http://arxiv.org/abs/2602.08289v1
• Date: Mon, 09 Feb 2026 05:46:11 GMT
• Title: Knowledge Augmented Entity and Relation Extraction for Legal Documents with Hypergraph Neural Network
• Authors: Binglin Wu, Xianneng Li,
• Abstract summary: We propose an entity and relation extraction algorithm based on hypergraph neural network (Legal-KAHRE) for drug-related judgment documents.<n>We construct a legal dictionary with judicial domain knowledge and integrate it into text encoding representation.<n> Experimental results on the CAIL2022 information extraction dataset demonstrate that our method significantly outperforms existing baseline models.
• Score: 1.446271016723962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the continuous progress of digitization in Chinese judicial institutions, a substantial amount of electronic legal document information has been accumulated. To unlock its potential value, entity and relation extraction for legal documents has emerged as a crucial task. However, existing methods often lack domain-specific knowledge and fail to account for the unique characteristics of the judicial domain. In this paper, we propose an entity and relation extraction algorithm based on hypergraph neural network (Legal-KAHRE) for drug-related judgment documents. Firstly, we design a candidate span generator based on neighbor-oriented packing strategy and biaffine mechanism, which identifies spans likely to contain entities. Secondly, we construct a legal dictionary with judicial domain knowledge and integrate it into text encoding representation using multi-head attention. Additionally, we incorporate domain-specific cases like joint crimes and combined punishment for multiple crimes into the hypergraph structure design. Finally, we employ a hypergraph neural network for higher-order inference via message passing. Experimental results on the CAIL2022 information extraction dataset demonstrate that our method significantly outperforms existing baseline models.

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