Powered Hawkes-Dirichlet过程：基于灵活时间先验的文本聚类
Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior
摘要：
文档的文本内容及其发布日期是相互交织的。例如，根据潜在的时间动态，一篇关于某个主题的新闻文章的发表会受到以前关于类似问题的出版物的影响。然而，当文本信息传达的信息很少或时间动态难以揭示时，检索有意义的信息可能具有挑战性。此外，文档的文本内容并不总是与其时间动态相关联。我们开发了一种灵活的方法来创建集群的文本文件，根据他们的内容和出版时间，动力Dirichlet-Hawkes过程（PDHP）。我们发现，PDHP产生显着更好的结果比国家的最先进的模型时，时间信息或文本内容是弱信息。PDHP还证实了文本内容和时间动态总是完美相关的假设。PDHP允许检索文本集群，时间集群，或两者的混合物时，它们不是高精度的。我们证明了PDHP推广了以前的工作-如Dirichlet-Hawkes过程（DHP）和均匀过程（UP）。最后，我们使用Reddit数据说明了在现实世界的应用程序中PDHP引起的DHP和UP的变化。
英文摘要：
The textual content of a document and its publication date are intertwined. For example, the publication of a news article on a topic is influenced by previous publications on similar issues, according to underlying temporal dynamics. However, it can be challenging to retrieve meaningful information when textual information conveys little information or when temporal dynamics are hard to unveil. Furthermore, the textual content of a document is not always linked to its temporal dynamics. We develop a flexible method to create clusters of textual documents according to both their content and publication time, the Powered Dirichlet-Hawkes process (PDHP). We show PDHP yields significantly better results than state-of-the-art models when temporal information or textual content is weakly informative. The PDHP also alleviates the hypothesis that textual content and temporal dynamics are always perfectly correlated. PDHP allows retrieving textual clusters, temporal clusters, or a mixture of both with high accuracy when they are not. We demonstrate that PDHP generalizes previous work --such as the Dirichlet-Hawkes process (DHP) and Uniform process (UP). Finally, we illustrate the changes induced by PDHP over DHP and UP in a real-world application using Reddit data.
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