基于混合伽玛-负二项过程的无限作者主题模型
Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process
摘要：
对文本语料库的边信息，即，作者、时间戳和情感标签等信息融合到传统的文本挖掘模型中，在信息检索、统计自然语言处理和机器学习领域引起了人们极大的兴趣。这些作品的一个分支是所谓的作者主题模型（ATM），它将作者的兴趣作为边信息纳入经典的主题模型。然而，现有的ATM需要增加主题的数量，这在许多现实世界的设置中是困难和不合适的。在本文中，我们提出了一个无限作者主题（IAT）模型来解决这个问题。我们使用一个随机过程来从数据本身确定主题的数量，而不是在固定数量的主题上分配一个离散的概率。具体来说，我们扩展了一个伽玛负二项过程，以捕捉作者-文档-关键字层次结构的三个层次。此外，每个文档都被分配了一个混合伽马过程，该过程考虑了多作者对该文档的贡献。针对IAT模型，提出了一种有效的Gibbs抽样推断算法，该算法的每个条件分布都是封闭形式的。在多个真实数据集上的实验表明，IAT模型能够同时学习隐藏主题、作者对这些主题的兴趣以及主题的数量。
英文摘要：
Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditional text mining models has gained significant interests in the area of information retrieval, statistical natural language processing, and machine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) model to resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determine the number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in order to capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma process that accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm with each conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets show the capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topics simultaneously.
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。
