A graphical decomposition and similarity measurement approach for topic detection from online news
A graphical decomposition and similarity measurement approach for topic detection from online news
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用于在线新闻主题检测的图形分解和相似性测量方法
DOI：
10.1016/j.ins.2021.04.029
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发表时间：
2021-04
影响因子：
8.1
通讯作者：
覃飙
中科院分区：
文献类型：
--
作者：
肖克晶;Zhaopeng Qian;覃飙
话题检测的目的是从海量的网络新闻中发现有价值的话题。它可以帮助人们捕捉现实世界中发生的事情，减轻信息过载的负担。由于网络新闻正经历着爆炸性的增长，这也具有重要的意义。话题检测通常被转化为文档聚类问题，其核心思想是基于文档相似度将报道相同主题的新闻文档聚类到同一组。由于新闻文档结构复杂、篇幅较长，新闻相似度的计算具有很大的挑战性。现有的基于术语的方法利用向量空间模型(VSM)来表示基于文档中一组信息性关键字的新闻文档，然后通过余弦相似度计算文档之间的关系。然而，VSM忽略了词与词之间的关系，具有稀疏的语义，导致话题检测的准确率较低。近年来，人们提出了概率方法和图分析方法来进行话题检测。然而，这两种算法都具有较高的时间复杂度。针对这些问题，我们首先提出了一种新的基于图形分解的文档表示方法，将每个新闻文档分解成不同的语义单元，然后构造这些语义单元之间的关系，形成一个胶囊语义图(CSG)。与VSM表示相比，CSG保留了词与词之间的关系，缓解了稀疏语义。接下来，我们引入图核来度量基于子结构的CSG之间的相似性。最后，采用增量聚类的方法对新闻文档进行聚类，其中文档用CSG表示，文档之间的相似度用图核来计算。在三个标准数据集上的实验结果表明，我们的方法在准确率、召回率和F1评分上都比几种最先进的方法获得了更高的精度。此外，在一个大型新闻数据集上的实验结果表明，我们的CSG-SM比概率方法和图分析方法具有更低的时间复杂度。
Topic detection aims to discover valuable topics from the massive online news. It can help people to capture what is happening in real world and alleviate the burden of information overload. It also has great significance since the online news is experiencing an explosive growth. Topic detection is typically transformed into a document clustering problem, whose core idea is to cluster news documents that report on the same topic to the same group based on document similarity. Due to the complex structure and long length of news documents, the similarity measurement of news is very challenging. Existing term-based methods represent news documents based on a set of informative keywords in the document with a vector space model (VSM) and then the relationship between documents is calculated by cosine similarity. However, VSM ignores the relationship between words and has sparse semantics, which leads to low precision of topic detection. In recent years, the probabilistic methods and the graph analytical methods have been proposed for topic detection. However, both of them have high time complexity. To cope with these problems, we first present a novel document representation approach based on graphical decomposition, which decomposes each news document into different semantic units and then relationship between the semantic units is constructed to form a capsule semantic graph (CSG). The CSG can retain the relationship between words and alleviate the sparse semantics compared to VSM representation. We next introduce the graph kernel to measure the similarity between the CSGs based on their substructures. Finally, we use an incremental clustering method to cluster the news documents, in which the documents are represented by CSGs and the similarity between documents is calculated by graph kernel. The experiment results on three standard datasets show that our method obtains higher precision, recall and F1 score than several state-of-the-art methods. Moreover, the experiment results on a large news dataset show that our CSG-SM has lower time complexity than probabilistic methods and graph analytical methods.