论文标题
文档级关系提取的基于双图的推理
Double Graph Based Reasoning for Document-level Relation Extraction
论文作者
论文摘要
文档级别的关系提取旨在提取文档中实体之间的关系。与句子级别的关系提取不同，它需要对文档的多个句子进行推理。在本文中，我们提出了具有双图的图形聚合和推导网络（增益）。增益首先构建了异质提及的图表（HMG），以模拟整个文档中不同提及之间的复杂相互作用。它还构建了一个实体级图（例如），基于我们提出一种新颖的路径推理机制来推断实体之间的关系。在公共数据集上进行的实验显示增益比以前的最新面前的性能改善（F1上的2.85）。我们的代码可在https://github.com/dreaminvoker/gain上找到。
Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across a document. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document. It also constructs an entity-level graph (EG), based on which we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/DreamInvoker/GAIN .
