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:在本文中,我们提议,句子中的关键语义信息在实体关系提取中发挥关键作用。我们提议假设句子中的关键语义信息在实体关系提取中起着关键作用。基于这一假设,我们根据实体在句子中的位置将句子分成三部分,并通过判决内注意机制发现句子中的精细语义特征,以减少不相干噪音信息的干扰。拟议的语义提取模型可以充分利用现有的正义语义信息。实验结果表明,拟议的语义提取模型提高了准确-回调曲线和P@N值,与现有方法相比,这证明了这一模式的有效性。