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.

翻译：关系抽取任务是自然语言处理中关键且具有挑战性的方面。近期涌现出多种方法，在处理该任务时展现出显著性能；然而，这些方法大多依赖于从大规模知识图谱或基于海量语料预训练的语言模型中获取的大数据。本文专注于仅利用语料库本身提供的知识来构建高性能模型。我们的目标是证明：在不引入外部知识的前提下，通过利用语料内实体的层级结构和关系分布，关系抽取模型能够实现显著更优的性能。为此，我们提出了一种将语料规模下的预训练知识图谱嵌入融入句子级上下文表示的关系抽取方法。通过一系列实验，该方法展现出富有前景且极具趣味性的结果。实验结果表明，与基于上下文的关系抽取模型相比，我们的方法表现更优。