Joint Self-Attention Based Neural Networks for Semantic Relation Extraction

• 作者: Jing He Jun Sun Lei Zhang Wenke Ding Xiajiong Shen Xianjin Shi Yan Li Yatian Shen
• 来源期刊: 信息隐藏与隐私保护杂志(英文)
• 学科: 工学
• 关键词: Self-attention RELATION extraction NEURAL networks
• 年，卷（期）: 2019,（2）
• 页码范围: 69-75
• 页数: 7页
• 分类号: TP3

摘要

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-ofthe-art classification accuracy just with minimal feature engineering.