We describe a new method for visualizing topics, the distributions over terms
that are automatically extracted from large text corpora using latent variable
models. Our method finds significant $n$-grams related to a topic, which are
then used to help understand and interpret the underlying distribution.
Compared with the usual visualization, which simply lists the most probable
topical terms, the multi-word expressions provide a better intuitive impression
for what a topic is "about." Our approach is based on a language model of
arbitrary length expressions, for which we develop a new methodology based on
nested permutation tests to find significant phrases. We show that this method
outperforms the more standard use of $\chi^2$ and likelihood ratio tests. We
illustrate the topic presentations on corpora of scientific abstracts and news
articles.

提出了一种用于主题可视化的新方法，使用潜在变量模型自动提取大型文本语料库中的 term 分布，并利用与主题相关的重要短语来解释该分布。该方法使用基于任意长度表达式的语言模型，并提出了一种基于嵌套排列检验的新方法来查找重要短语。结果表明，该方法优于标准的卡方检验和似然比检验。应用在科学摘要和新闻文章的语料库上，演示了主题的呈现。