Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany). RE models usually ignore such readily available side information. In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available. Through extensive experiments on benchmark datasets, we demonstrate RESIDE's effectiveness. We have made RESIDE's source code available to encourage reproducible research.
翻译:远程监控的Relation Expliton (RE) 方法通过将知识库(KB)中的关系实例与无结构文本自动匹配,对提取器进行培训。除了关系实例外, KB 通常包含其他相关的侧面信息, 如关系别名(例如, 创建和共同创建是关系创始人的别名 Company) 。 RE 模型通常忽略这种现成的侧面信息。 在本文中, 我们提议 REIDE, 这是一种远程监控的神经关系提取方法, 利用 KBs 的额外侧端信息来改进关系提取。 它使用实体类型和别名信息在预测关系时施加软约束。 RESIDE 使用图象变动网络( GCN ) 将文本中的合成信息编码, 并即使在可获得有限的侧面信息时也提高性能。 我们通过对基准数据集的广泛实验, 演示RESIDE 的有效性。 我们提供了RESIDE 源代码, 以鼓励进行可复制的研究 。