Open Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models have been developed based on syntactic structures of sentences, identified by syntactic parsers. However, previous neural OpenIE models under-explore the useful syntactic information. In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures. To better fuse heterogeneous information from both graphs, we adopt multi-view learning to capture multiple relationships from them. Finally, the finetuned constituency and dependency representations are aggregated with sentential semantic representations for tuple generation. Experiments show that both constituency and dependency information, and the multi-view learning are effective.
翻译:开放信息提取系统(OpenIE)旨在从开放式句子中提取关系图。传统的基于规则的或统计模型是根据综合判决结构开发的,由同理学者确定。然而,先前的神经开放模型在爆炸后未开发出有用的合成信息。在本文中,我们将对象和依赖树都建成文字级图,并使神经开放信息能够从合成结构中学习。为了更好地整合两个图表中的不同信息,我们采用了多视角学习方法,从中获取多种关系。最后,微调的支持者和依赖性代表方式与对图普勒一代的感性语义表达方式相结合。实验表明,对象和依赖性信息以及多视角学习都是有效的。