Surface pattern-enhanced relation extraction with global constraints

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.