In recent years, the extraction of overlapping relations has received great attention in the field of natural language processing (NLP). However, most existing approaches treat relational triples in sentences as isolated, without considering the rich semantic correlations implied in the relational hierarchy. Extracting these overlapping relational triples is challenging, given the overlapping types are various and relatively complex. In addition, these approaches do not highlight the semantic information in the sentence from coarse-grained to fine-grained. In this paper, we propose an end-to-end neural framework based on a decomposition model that incorporates multi-granularity relational features for the extraction of overlapping triples. Our approach employs an attention mechanism that combines relational hierarchy information with multiple granularities and pretrained textual representations, where the relational hierarchies are constructed manually or obtained by unsupervised clustering. We found that the different hierarchy construction strategies have little effect on the final extraction results. Experimental results on two public datasets, NYT and WebNLG, show that our mode substantially outperforms the baseline system in extracting overlapping relational triples, especially for long-tailed relations.

近年来，重叠关系的提取在自然语言处理（NLP）领域受到了极大的关注。然而，大多数现有方法将句子中的关系三元组视为孤立的，而没有考虑关系层次结构中隐含的丰富语义相关性。鉴于重叠类型多种多样且相对复杂，提取这些重叠关系三元组具有挑战性。此外，这些方法没有从粗粒度到细粒度突出句子中的语义信息。在本文中，我们提出了一种基于分解模型的端到端神经框架，该框架结合了多粒度关系特征来提取重叠三元组。我们的方法采用了一种注意力机制，将关系层次结构信息与多粒度和预训练的文本表示相结合，其中关系层次结构是手动构建的或通过无监督聚类获得的。我们发现不同的层次结构构建策略对最终的提取结果影响不大。在两个公共数据集 NYT 和 WebNLG 上的实验结果表明，我们的模式在提取重叠关系三元组方面远远优于基线系统，特别是对于长尾关系。