论文标题
与自定义的图形卷积网络提取的关系提取
Relation Extraction with Self-determined Graph Convolutional Network
论文作者
论文摘要
关系提取是获得文本中实体之间语义关系的一种方式。最新的方法使用语言工具来为实体出现的文本构建图形，然后使用图形卷积网络（GCN）来编码预构建的图形。尽管他们的表现很有希望，但对语言工具的依赖会导致非端到端过程。在这项工作中，我们提出了一个新型模型，即自决的图形卷积网络（SGCN），该网络使用自我发项机制来确定加权图，而不是使用任何语言工具。然后，使用GCN编码自确定的图。我们在Tacred数据集上测试我们的模型，并实现最先进的结果。我们的实验表明，SGCN优于传统GCN，该GCN使用依赖性解析工具来构建图形。
Relation Extraction is a way of obtaining the semantic relationship between entities in text. The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear and then a Graph Convolutional Network (GCN) is employed to encode the pre-built graphs. Although their performance is promising, the reliance on linguistic tools results in a non end-to-end process. In this work, we propose a novel model, the Self-determined Graph Convolutional Network (SGCN), which determines a weighted graph using a self-attention mechanism, rather using any linguistic tool. Then, the self-determined graph is encoded using a GCN. We test our model on the TACRED dataset and achieve the state-of-the-art result. Our experiments show that SGCN outperforms the traditional GCN, which uses dependency parsing tools to build the graph.
