Connections between relations in relation extraction, which we call class
ties, are common. In distantly supervised scenario, one entity tuple may have
multiple relation facts. Exploiting class ties between relations of one entity
tuple will be promising for distantly supervised relation extraction. However,
previous models are not effective or ignore to model this property. In this
work, to effectively leverage class ties, we propose to make joint relation
extraction with a unified model that integrates convolutional neural network
(CNN) with a general pairwise ranking framework, in which three novel ranking
loss functions are introduced. Additionally, an effective method is presented
to relieve the severe class imbalance problem from NR (not relation) for model
training. Experiments on a widely used dataset show that leveraging class ties
will enhance extraction and demonstrate the effectiveness of our model to learn
class ties. Our model outperforms the baselines significantly, achieving
state-of-the-art performance.

本文提出了一种基于卷积神经网络和普通成对排名框架的统一模型，引入三种新的排名损失函数和有效的方法来解决模型训练中的类别不平衡问题，成功地利用关系提取中的类别关系提高了抽取效果，并表明了模型学习类别关系的有效性和优越性。