Unsupervised Web Topic Detection Using A Ranked Clustering-Like Pattern Across Similarity Cascades
Unsupervised Web Topic Detection Using A Ranked Clustering-Like Pattern Across Similarity Cascades
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DOI：
10.1109/tmm.2015.2425143
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发表时间：
2015-04
影响因子：
7.3
通讯作者：
Junbiao Pang;Fei Jia;Chunjie Zhang;W. Zhang;Qingming Huang;Baocai Yin
中科院分区：
文献类型：
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作者：
Junbiao Pang;Fei Jia;Chunjie Zhang;W. Zhang;Qingming Huang;Baocai Yin
尽管互联网上的社交媒体大规模增长，但组织、理解和监控用户生成内容（UGC）的过程已成为当今社会最紧迫的问题之一。从大量的UGC中发现网络上的主题是实现这一目标的有希望的方法之一。与经典的新闻文章中的主题检测和跟踪相比，由于互联网上的数据具有噪声、稀疏和约束较少的特点，识别网络上的主题绝非易事。在本文中，我们从相似性扩散的角度来研究方法，并提出了一个类似于聚类模式的相似性级联（SC）。SC是通过用一组阈值截断相似图而生成的一系列子图，然后使用最大团来捕获主题。最后，提出了一个主题限制的相似性扩散过程，以有效地识别真实的主题从大量的候选人。实验表明，我们的方法优于国家的最先进的方法在三个公共数据集。
Despite the massive growth of social media on the Internet, the process of organizing, understanding, and monitoring user generated content (UGC) has become one of the most pressing problems in today's society. Discovering topics on the web from a huge volume of UGC is one of the promising approaches to achieve this goal. Compared with classical topic detection and tracking in news articles, identifying topics on the web is by no means easy due to the noisy, sparse, and less- constrained data on the Internet. In this paper, we investigate methods from the perspective of similarity diffusion, and propose a clustering-like pattern across similarity cascades (SCs). SCs are a series of subgraphs generated by truncating a similarity graph with a set of thresholds, and then maximal cliques are used to capture topics. Finally, a topic-restricted similarity diffusion process is proposed to efficiently identify real topics from a large number of candidates. Experiments demonstrate that our approach outperforms the state-of-the-art methods on three public data sets.