A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling

TL;DR

This paper introduces a global feature-oriented relational triple extraction model that leverages global associations to improve extraction accuracy, achieving state-of-the-art results on benchmark datasets.

Contribution

The proposed model incorporates global relation and token pair associations into table filling for relational triple extraction, enhancing performance over existing local-feature methods.

Findings

Achieves state-of-the-art results on three benchmark datasets.

Effectively integrates global associations into the triple extraction process.

Demonstrates significant performance improvement over local-feature based methods.

Abstract

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.