Visualizing Topics with Multi-Word Expressions

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 $n$-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.