结合大语言模型生成支撑文档的图增强关系抽取模型
Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document
AI总结：
本研究提出一种结合LLMs生成支撑文档与GNN的句子级关系抽取新方法，通过构建文本图表示优化实体嵌入，在CrossRE数据集多领域取得显著性能提升，为关系抽取领域发展提供新思路。
摘要：
本研究提出了一种句子级关系抽取（RE）新方法，该方法将图神经网络（GNNs）与大语言模型（LLMs）相结合，以生成上下文丰富的支撑文档。通过利用LLMs生成辅助信息的能力，我们的方法构建了文本数据的复杂图表示。该图随后通过图神经网络（GNN）进行处理，以优化并丰富每个实体对应的嵌入，确保对数据形成更细致、更具关联性的理解。该方法通过引入更广泛的上下文并利用实体间的交互作用，解决了传统句子级RE模型的局限性，从而提升了模型捕捉跨句复杂关系的能力。我们在CrossRE数据集上开展的实验证明了该方法的有效性，在多个领域的性能均有显著提升。研究结果凸显了将GNNs与LLM生成的上下文相结合以推动关系抽取领域发展的潜力。
英文摘要：
This study introduces a novel approach to sentence-level relation extraction (RE) that integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate contextually enriched support documents. By harnessing the power of LLMs to generate auxiliary information, our approach crafts an intricate graph representation of textual data. This graph is subsequently processed through a Graph Neural Network (GNN) to refine and enrich the embeddings associated with each entity ensuring a more nuanced and interconnected understanding of the data. This methodology addresses the limitations of traditional sentence-level RE models by incorporating broader contexts and leveraging inter-entity interactions, thereby improving the model's ability to capture complex relationships across sentences. Our experiments, conducted on the CrossRE dataset, demonstrate the effectiveness of our approach, with notable improvements in performance across various domains. The results underscore the potential of combining GNNs with LLM-generated context to advance the field of relation extraction.
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