##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.