• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Automated Social Text Annotation With Joint Multilabel Attention Networks
【24h】

Automated Social Text Annotation With Joint Multilabel Attention Networks

机译：具有联合多标签关注网络的自动化社会文本注释

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of F-1, JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6% and the hierarchical attention network (HAN) by around 3.9%-23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F-1 was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN(d)) has the potential to further improve the performance of JMAN but at the cost of substantial memory and warrants further study.
机译：自动社交文本注释是表明一组标签上的社交媒体平台的共享文档的任务。自动化注释过程可以减少用户的认知开销标记，提高标签管理更好的搜索，浏览和文件的建议。它可以配制成多标记分类问题。我们提出这个问题的新深学习法，并与基于语义的正规化，它可以模拟用户的阅读和注释的行为，制定更好的文档表示，利用标签之间的语义关系，设计了一个基于注意机制的神经网络。网络单独模型标题和每个文档的内容和注入明确，标题引导注意力机制引入每个句子。为了利用标签之间的相关性，我们提出了两种基于语义的损失regularizers，即，相似性和包容，其执行网络的输出以符合标签语义。与基于语义的损失regularizers该模型被称为联合多标记关注网络（JMAN）。我们进行了全面的评估研究和比较JMAN到国家的最先进的基线模型，使用四个大的，真实世界的社交媒体数据集。在F-1而言，JMAN显著比较了约12.8％-78.6％和层次关注网络（HAN）的周围3.9％-23.8％，跑赢双向门重复单元（双GRU）。该JMAN模型表明收敛和训练速度快等优点。观察到对潜狄利克雷分配（LDA）和支持向量机（SVM）的性能的进一步改进。当应用基于语义的损失regularizers，韩和Bi-GRU在F-1方面的表现也提振。研究还发现，标签语义矩阵的动态更新（JMAN（d））有进一步提高JMAN的性能，但是在大量存储器和值得进一步研究的费用的潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号