Scalable and Robust Construction of Topical Hierarchies

TL;DR

This paper introduces a scalable, robust algorithm for automatically generating high-quality topical hierarchies from large text collections, enabling efficient and interactive knowledge organization.

Contribution

It presents a novel top-down recursive framework using tensor orthogonal decomposition for hierarchical topic modeling, significantly improving scalability and robustness.

Findings

Reduces hierarchy construction time by several orders of magnitude

Generates high-quality, robust topical hierarchies

Enables interactive hierarchy revision

Abstract

Automated generation of high-quality topical hierarchies for a text collection is a dream problem in knowledge engineering with many valuable applications. In this paper a scalable and robust algorithm is proposed for constructing a hierarchy of topics from a text collection. We divide and conquer the problem using a top-down recursive framework, based on a tensor orthogonal decomposition technique. We solve a critical challenge to perform scalable inference for our newly designed hierarchical topic model. Experiments with various real-world datasets illustrate its ability to generate robust, high-quality hierarchies efficiently. Our method reduces the time of construction by several orders of magnitude, and its robust feature renders it possible for users to interactively revise the hierarchy.