摘要
In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.
作者
徐翼龙
Li Wenfa
Wang Gongming
Huang Lingyun
Xu Yilong;Li Wenfa;Wang Gongming;Huang Lingyun(Smart City College,Beijing Union University,Beijing 100101,P.R.China;College of Robotics,Beijing Union University,Beijing 100101,P.R.China;Tianyuan Network Co.,Ltd.,Beijing 100193,P.R.China;Beijing Tianyuan Network Co.,Ltd.,Beijing 100193,P.R.China;Chinatelecom Information Development Co.,Ltd.,Beijing 100093,P.R.China)
基金
Supported by the National Natural Science Foundation of China(No.61972040)
the Science and Technology Projects of Beijing Municipal Education Commission(No.KM201711417011)
the Premium Funding Project for Academic Human Resources Development in Beijing Union University(No.BPHR2020AZ03)。