A CORRELATED TOPIC MODEL OF SCIENCE
A CORRELATED TOPIC MODEL OF SCIENCE
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DOI：
10.1214/07-aoas114
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发表时间：
2007-06-01
影响因子：
1.8
通讯作者：
Lafferty, John D.
中科院分区：
文献类型：
--
作者：
Blei, David M.;Lafferty, John D.
主题模型。如潜在狄利克雷分配（LDA），称之为有用的工具，统计分析的文件收集和其他离散数据。LDA模型假设每个文档的单词都来自于一个主题的混合，每个主题都分布在词汇表上。LDA的一个局限性是无法对主题相关性进行建模，即使例如，一个关于遗传学的文档比X射线天文学更有可能也是关于疾病的。这种局限性源于使用Dirichlet分布来模拟主题比例之间的变化。在本文中，我们开发了相关主题模型（CTM），其中主题比例通过logistic正态分布表现出相关性[J. Roy.中央集权主义者Soc. Ser. B 44（1982）139-177]。在此模型中，我们得到了一个快速变分推理算法的近似后验推理。这是复杂的事实，即逻辑斯谛常态是不共轭的多项式。我们将CTM应用于1990-1999年科学出版的文章，一个数据集，包括57万字。CTM提供了一个更好的适合的数据比LDA，我们证明了它的使用作为一个探索性的工具，大型文档集。
Topic models. such as latent Dirichlet allocation (LDA), call he useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is it distribution over the vocabulary. A limitation of LDA is the inability to model topic correlation even though, for example, a document about genetics is more likely to also be about disease than X-ray astronomy. This limitation Sterns from the use of the Dirichlet distribution to model the variability among the topic proportions. In this paper we develop the correlated topic model (CTM), where the topic proportions exhibit correlation via the logistic normal distribution [J. Roy. Statist. Soc. Ser. B 44 (1982) 139-177]. We derive a fast variational inference algorithm for approximate posterior inference in this model. which is complicated by the fact that the logistic normal is not conjugate to the multinomial. We apply the CTM to the articles from Science Published from 1990-1999, a data set that comprises 57M words. The CTM gives a better fit of the data than LDA, and we demonstrate its Use as an exploratory tool of large document collections.