Leveraging Knowledge Graph Embeddings to Enhance Contextual
  Representations for Relation Extraction
Fréjus A. A. Laleye Loïc Rakotoson Sylvain Massip
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.