##Statistics Seminar##\\ Department of Mathematical Sciences ^ **DATE:**|Thursday, Month 31, 2017 | ^ **TIME:**|1:15pm -- 2:15pm | ^ **LOCATION:**|WH 100E | ^ **SPEAKER:**|Fan Yang, Binghamton University | ^ **TITLE:**|Visualizing Topics with Multi-Word Expressions | \\ **Abstract** 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-square and likelihood ratio tests. We illustrate the topic presentations on corpora of scientific abstracts and news articles.