Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes
Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes
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，它可以是具有挑战性的检索有意义的信息时，文本信息传达很少。此外，文档的文本内容并不总是与其时间动态相关。我们开发了一种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）。当时间信息或文本内容是弱信息时，PDHP比最先进的模型产生更好的结果。PDHP还证实了文本内容和时间动态完全相关的假设。我们证明，PDHP概括了以前的工作-如DHP和UP。最后，我们使用Reddit的真实数据集来说明一个可能的应用程序。
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. Furthermore, the textual content of a document is not always correlated to its temporal dynamics. We develop a method to create clusters of textual documents according to both their content and publication time, the Powered Dirichlet-Hawkes process (PDHP). PDHP yields significantly better results than state-of-the-art models when temporal information or textual content is weakly informative. PDHP also alleviates the hypothesis that textual content and temporal dynamics are perfectly correlated. We demonstrate that PDHP generalizes previous work --such as DHP and UP. Finally, we illustrate a possible application using a real-world dataset from Reddit.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
