TY  - EJOU
AU  - Sun, Jun  
AU  - Li, Yan  
AU  - Shen, Yatian  
AU  - Ding, Wenke  
AU  - Shi, Xianjin  
AU  - Zhang, Lei  
AU  - Shen, Xiajiong  
AU  - He, Jing  

TI  - Joint Self-Attention Based Neural Networks for Semantic Relation Extraction
T2  - Journal of Information Hiding and Privacy Protection

PY  - 2019
VL  - 1
IS  - 2
SN  - 2637-4226

AB  - Relation extraction is an important task in NLP community. However, some models often fail in capturing Long-distance dependence on semantics, and the interaction between semantics of two entities is ignored. In this paper, we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM (SA-Bi-LSTM) to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information, and capture Long-distance dependence on semantics. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrated that the proposed method is effective against relation classification, which can obtain state-of-the-art classification accuracy just with minimal feature engineering.
KW  - Self-attention
KW  -  relation extraction
KW  -  neural networks

DO  - 10.32604/jihpp.2019.06357