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 ${\chi }^2$ and likelihood ratio tests. We illustrate the topic presentations on corpora of scientific abstracts and news articles.

References in corpus (2)

• Probabilistic Latent Semantic Analysis
• Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression

Cited by in corpus (5)

• CommunityClick: Capturing and Reporting Community Feedback from Town Halls to Improve Inclusivity
• Scalable Generalized Dynamic Topic Models
• Scalable Topical Phrase Mining from Text Corpora
• Scalable and Robust Construction of Topical Hierarchies
• Combining Thesaurus Knowledge and Probabilistic Topic Models