| Xu Yilong (徐翼龙)*,Li Wenfa**,Wang Gongming*** ****,Huang Lingyun*****.[J].高技术通讯(英文),2020,26(4):442~447 |
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| A multi-target stance detection based on Bi-LSTM network with position-weight |
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| DOI：10.3772/j.issn.1006-6748.2020.04.012 |
| 中文关键词: |
| 英文关键词: long short-term memory (LSTM), multi-target, natural language processing, stance detection |
| 基金项目: |
| Author Name | Affiliation | | Xu Yilong (徐翼龙)* | （*Smart City College, Beijing Union University, Beijing 100101, P.R.China） | | Li Wenfa** | （**College of Robotics, Beijing Union University, Beijing 100101, P.R.China） | | Wang Gongming*** **** | （***Tianyuan Network Co., Ltd., Beijing 100193, P.R.China）
（****Beijing Tianyuan Network Co., Ltd., Beijing 100193, P.R.China） | | Huang Lingyun***** | （*****Chinatelecom Information Development Co., Ltd., Beijing 100093, P.R.China） |
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| 中文摘要: |
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| 英文摘要: |
| 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. |
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