论文标题
与远处有监督关系提取的力图的学习关系联系
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
论文作者
论文摘要
关系关系定义为不同关系之间的相关性和相互排斥，对于遥远的监督关系提取至关重要。现有方法通过贪婪地学习当地依赖性来对该属性进行建模。但是，由于未能捕获关系关系的全球拓扑结构，它们基本上受到限制。结果，它们很容易落入本地最佳解决方案。为了解决这个问题，在本文中，我们提出了一种基于力量的新型基于图的关系提取模型，以全面学习关系。具体而言，我们首先根据关系的全局共发生构建图形。然后，我们从物理学中借用了库仑定律的概念，并将吸引力和排斥力的概念引入该图，以学习关系之间的相关性和相互排斥。最后，获得的关系表示形式被用作相互依赖性的关系分类器。大规模基准数据集的实验结果表明，我们的模型能够建模全球关系关系并显着优于其他基准。此外，提出的实力图形可以用作增强现有关系提取系统并提高其性能的模块。
Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Existing approaches model this property by greedily learning local dependencies. However, they are essentially limited by failing to capture the global topology structure of relation ties. As a result, they may easily fall into a locally optimal solution. To solve this problem, in this paper, we propose a novel force-directed graph based relation extraction model to comprehensively learn relation ties. Specifically, we first build a graph according to the global co-occurrence of relations. Then, we borrow the idea of Coulomb's Law from physics and introduce the concept of attractive force and repulsive force to this graph to learn correlation and mutual exclusion between relations. Finally, the obtained relation representations are applied as an inter-dependent relation classifier. Experimental results on a large scale benchmark dataset demonstrate that our model is capable of modeling global relation ties and significantly outperforms other baselines. Furthermore, the proposed force-directed graph can be used as a module to augment existing relation extraction systems and improve their performance.
