The main purpose of relation extraction is to extract the semantic relationships between tagged pairs of entities in a sentence, which plays an important role in the semantic understanding of sentences and the construction of knowledge graphs. In this paper, we propose that the key semantic information within a sentence plays a key role in the relationship extraction of entities. We propose the hypothesis that the key semantic information inside the sentence plays a key role in entity relationship extraction. And based on this hypothesis, we split the sentence into three segments according to the location of the entity from the inside of the sentence, and find the fine-grained semantic features inside the sentence through the intra-sentence attention mechanism to reduce the interference of irrelevant noise information. The proposed relational extraction model can make full use of the available positive semantic information. The experimental results show that the proposed relation extraction model improves the accuracy-recall curves and P@N values compared with existing methods, which proves the effectiveness of this model.

翻译：关系抽取的主要目的是提取句子中标记实体对之间的语义关系，这在句子语义理解和知识图谱构建中扮演着重要角色。本文提出句子内部的关键语义信息在实体关系抽取中起着关键作用。我们提出假设：句子内部的语义关键信息在实体关系抽取中起着关键作用。基于该假设，我们根据句子内部实体的位置将句子切分为三个片段，并通过句内注意力机制挖掘句子内部的细粒度语义特征，以减少无关噪声信息的干扰。所提出的关系抽取模型能够充分利用可用的正向语义信息。实验结果表明，与现有方法相比，该关系抽取模型在精确率-召回率曲线和P@N值上均有所提升，证明了该模型的有效性。