Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction
This addresses the problem of relation extraction in NLP by reducing reliance on external data, though it is incremental as it builds on existing embedding and contextual methods.
The paper tackled relation extraction by incorporating pretrained knowledge graph embeddings from the corpus itself into sentence-level contextual representations, achieving significantly enhanced performance compared to context-based models.
Relation extraction task is a crucial and challenging aspect of Natural Language Processing. Several methods have surfaced as of late, exhibiting notable performance in addressing the task; however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. Our objective is to showcase that by leveraging the hierarchical structure and relational distribution of entities within a corpus without introducing external knowledge, a relation extraction model can achieve significantly enhanced performance. We therefore proposed a relation extraction approach based on the incorporation of pretrained knowledge graph embeddings at the corpus scale into the sentence-level contextual representation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach.The obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.