Abstract
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.
Abstract (translated)
关系提取的主要目的是从句子中提取实体之间的语义关系,这在句子的语义理解和知识图的构建中发挥着重要作用。在本文中,我们提出句子内部的关键语义信息在实体关系提取中发挥着关键作用。我们提出了假设,即句子内部的关键语义信息在实体关系提取中发挥着关键作用。基于这个假设,我们根据句子中的实体位置将句子划分为三个部分,并通过句子内部注意力机制找到句子中的精细语义特征,以减少无关噪声信息的影响。我们提出的关系提取模型能够充分利用可用的积极语义信息。实验结果显示,与现有方法相比,我们提出的关系提取模型提高了精度回忆曲线和P@N值,证明了该模型的有效性。
URL
https://arxiv.org/abs/2302.02078
