Relation extraction is one of the most important tasks in information extraction. The traditional works either use sentences or surface patterns (i.e., the shortest dependency paths of sentences) to build extraction models. Intuitively, the integration of these two kinds of methods will further obtain more robust and effective extraction models, which is, however, ignored in most of the existing works. In this paper, we aim to learn the embeddings of surface patterns to further augment the sentence-based models. To achieve this purpose, we propose a novel pattern embedding learning framework with the weighted multi-dimensional attention mechanism. To suppress noise in the training dataset, we mine the global statistics between patterns and relations and introduce two kinds of prior knowledge to guide the pattern embedding learning. Based on the learned embeddings, we present two augmentation strategies to improve the existing relation extraction models. We conduct extensive experiments on two popular datasets (i.e., NYT and KnowledgeNet) and observe promising performance improvements.

关系提取是信息提取中最重要的任务之一。传统作品要么使用句子，要么使用表面模式（即句子的最短依赖路径）来构建提取模型。直观地讲，这两种方法的集成将进一步获得更健壮和有效的提取模型，但是，在大多数现有工作中都忽略了这一模型。在本文中，我们旨在学习表面图案的嵌入，以进一步增强基于句子的模型。为了达到这个目的，我们提出了一种具有加权多维注意力机制的新型模式嵌入学习框架。为了抑制训练数据集中的噪声，我们挖掘了模式和关系之间的全局统计信息，并介绍了两种先验知识来指导模式嵌入学习。基于学习的嵌入，我们提出了两种扩充策略来改进现有的关系提取模型。我们在两个流行的数据集（即NYT和KnowledgeNet）上进行了广泛的实验，并观察到了有希望的性能改进。