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Statistics Seminar
Department of Mathematical Sciences

DATE:Thursday, Month 31, 2017
TIME:1:15pm – 2:15pm
SPEAKER:Fan Yang, Binghamton University
TITLE:Visualizing Topics with Multi-Word Expressions


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.

seminars/stat/181108.txt · Last modified: 2018/10/31 18:15 by qyu