PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2106.09895v1)

Abstract: Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.

Analysis of PRGC: Potential Relation and Global Correspondence-Based Joint Relational Triple Extraction

The paper "PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction" introduces an innovative framework for joint extraction of entities and relations from unstructured texts. Addressing several inherent limitations found in current approaches, the authors have made considerable strides in optimizing the extraction process for relational triples (subject, predicate, object) - a fundamental aspect of information extraction tasks.

Methodological Innovations

The core contribution of the paper is the PRGC framework, which decomposes the relational triple extraction task into three distinct subtasks: Relation Judgement, Entity Extraction, and Subject-object Alignment. The novel approach used by PRGC facilitates efficient and accurate extraction while addressing redundancy and generalization issues pervasive in existing models.

1. Relation Judgement: The framework begins by predicting potential relations within a sentence rather than examining all possible relations, significantly reducing computational overhead and improving accuracy.
2. Entity Extraction: Employing a relation-specific sequence tagging strategy, the framework robustly extracts subjects and objects, effectively managing scenarios with overlapping entities to ensure reliable generalization.
3. Subject-object Alignment: A global correspondence matrix is employed to align subjects and objects into triples. This low-complexity method circumvents sparsity and inefficiency found in prior models.

Experimental Findings

Through extensive experimentation on benchmark datasets like NYT and WebNLG, PRGC demonstrates state-of-the-art performance with fewer parameters and higher inference efficiency compared to leading models like TPLinker and CasRel. Notably, the paper highlights PRGC's significant efficiency benefits, with advantages seen in complexity, floating point operations (FLOPs), parameter count, and inference time.

While TPLinker and CasRel have been evidently successful in their extraction tasks, PRGC establishes itself with superior convergence rates and improved handling of complex overlapping scenarios such as single entity overlap, entity pair overlap, and subject-object overlap, which often challenge traditional approaches.

Implications and Future Directions

The paper sets a precedent for examining relational triple extraction from a more granular and modular perspective, opening avenues for enhancements in large-scale information extraction applications. Beyond immediate practical implications, PRGC's innovative approach holds promise for integrating deeper semantic understanding into NLP models, potentially influencing advancements in knowledge graph construction, automated reasoning, and AI-driven content analysis.

Future research building on PRGC could explore further optimization techniques, expanding the framework's applicability to diverse textual and domain-specific datasets. Additionally, refining the global correspondence matrix's precision and recall metrics may enhance subject-object alignment further, unlocking new potential for comprehensive extraction systems in artificial intelligence.

The rigor, depth, and novel approach of the PRGC framework place it as a significant contribution in the domain of relational triple extraction, meriting attention and consideration for future developments in the field.