Visualizing Topics with Multi-Word Expressions
David M. Blei, John D. Lafferty

TL;DR
This paper introduces a novel visualization technique for topics that highlights significant multi-word expressions, improving interpretability of topic models over traditional term lists.
Contribution
The paper presents a new method using nested permutation tests to identify significant multi-word expressions for better topic visualization.
Findings
Outperforms standard $ ext{chi}^2$ and likelihood ratio tests in identifying significant phrases.
Enhances understanding of topics through more intuitive multi-word expressions.
Demonstrated effectiveness on scientific abstracts and news articles.
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 -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 and likelihood ratio tests. We illustrate the topic presentations on corpora of scientific abstracts and news articles.
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Topic Modeling · Natural Language Processing Techniques
