  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.
