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.

本文聚焦于关系抽取，提出假设：句子中关键语义信息对实体关系抽取起着关键作用，通过内部注意机制提取句子内细粒度的语义特征，证明该关系抽取模型比已有方法具有更优的准确率和召回率。