Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction. To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations. Specifically, we first generate a table feature for each relation. Then two kinds of global associations are mined from the generated table features. Next, the mined global associations are integrated into the table feature of each relation. This "generate-mine-integrate" process is performed multiple times so that the table feature of each relation is refined step by step. Finally, each relation's table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table. We evaluate the proposed model on three benchmark datasets. Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets. The source code of our work is available at: https://github.com/neukg/GRTE.
翻译:填补基于关系三重提取方法的表格正在吸引越来越多的研究兴趣,因为这些方法表现良好,而且有能力从复杂的句子中提取三重内容,因此吸引了越来越多的研究兴趣。然而,这类方法远远没有充分发挥潜力,因为大多数方法仅侧重于使用地方特征,而忽视了全球关系和配对关系的联系,这就增加了在三重提取过程中忽略一些重要信息的可能性。为了克服这一缺陷,我们提出了一个全球地貌导向三重提取模型,充分利用上述两种全球协会。具体地说,我们首先为每种关系制作一个表格特征。然后,从生成的表格特征中挖掘出两种全球协会。接下来,将埋设的全球协会纳入每个关系的表格特征中。这个“基因-地雷-混凝土”进程是多次进行的,这样每个关系的表格特征就能够逐步地得到完善。最后,每个关系表都以其精细的表特征为基础填写,所有与这一关系相关的三重数据都根据其填好的表格进行提取。我们评估了三个基准数据集的拟议模型。实验结果显示我们的模型是有效的,它实现了每个关系中的状态,并在所有数据源上实现了 MAGR/GR 。