FGSI: Distant Supervision for Relation Extraction method based on Fine-Grained Semantic Information
This work addresses relation extraction for semantic understanding and knowledge graph construction, presenting an incremental improvement over prior methods.
The paper tackled relation extraction by focusing on fine-grained semantic information within sentences, splitting them into segments based on entity locations and using an intra-sentence attention mechanism to reduce noise; experimental results showed improvements in accuracy-recall curves and P@N values compared to existing methods.
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.