Knowledge Commons of Institute of Automation,CAS
A Target-Guided Neural Memory Model for Stance Detection in Twitter | |
Penghui Wei1,2; Wenji Mao1,2; Daniel Zeng1,2 | |
2018-07 | |
会议名称 | 2018 International Joint Conference on Neural Networks (IJCNN) |
会议日期 | 2018-7 |
会议地点 | Rio de Janeiro, Brazil |
出版者 | IEEE |
摘要 | Exploring user stances and attitudes is beneficial to a number of Web related research and applications, especially in social media platforms such as Twitter. Stance detection in Twitter aims at identifying the stance expressed in a tweet towards a given target (e.g., a government policy). A key challenge of this task is that a tweet may not explicitly express opinion about the target. To effectively detect user stances implied in tweets, target content information plays an important role. In previous studies, conventional feature-based methods often ignore target content. Although more recent neural network-based methods attempt to integrate target information using attention mechanism, the performance improvement is rather limited due to the underuse of this information. To address this issue, we propose an end-to- end neural model, TGMN-CR, which makes better use of target content information. Specifically, our model first learns conditional tweet representation with respect to specific target. It then employs a target-guided iterative process to extract crucial stance-indicative clues via multiple interactions between target and tweet words. Experimental results on SemEval-2016 Task 6.A Twitter Stance Detection dataset show that our proposed method outperforms the state-of-the-art alternative methods, and substantially outperforms the comparative methods when a tweet does not explicitly express opinion about the given target. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44754 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Wenji Mao |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Penghui Wei,Wenji Mao,Daniel Zeng. A Target-Guided Neural Memory Model for Stance Detection in Twitter[C]:IEEE,2018. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
[2018IJCNN] A Target(2842KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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