An Effective Knowledgeable Label-Aware Approach for Sentential Relation Extraction

被引:0
|
作者
Nie, Binling [1 ]
Shao, Yiming [1 ]
机构
[1] Hangzhou Dianzi Technol Univ, Digital Media Sch, Hangzhou 310000, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 21期
关键词
sentential relation extraction; knowledge graph; label-aware;
D O I
10.3390/app132111929
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In recent years, sentential relation extraction has made remarkable progress with text and knowledge graphs (KGs). However, existing architectures ignore the valuable information contained in relationship labels, which KGs provide and can complement the model with additional signals and prior knowledge. To address this limitation, we propose a neural architecture that leverages knowledgeable labels to enhance sentential relation extraction. We name our proposed method knowledge label-aware sensitive relation extraction (KLA-SRE). To achieve this, we combine pre-trained static knowledge graph embeddings with learned semantic embeddings from other tokens to efficiently represent relation labels. By combining static pre-trained graph embeddings with learned word embeddings, we mitigate the inconsistency between predicted relations and given entities. Experimental results on various relation extraction benchmarks in different fields show that knowledge labels improve the F1 score by 1.6% and 1.1% on average over the baseline on standard- and minority-shot benchmarks, respectively.
引用
收藏
页数:13
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