# Scalable Inference for Nested Chinese Restaurant Process Topic Models

## TL;DR

This paper introduces a scalable inference algorithm for nested Chinese restaurant process topic models, enabling efficient processing of massive text corpora while maintaining high model quality.

## Contribution

A novel partially collapsed Gibbs sampling algorithm combined with an efficient distributed implementation for large-scale nCRP topic models.

## Key findings

- 111 times more efficient than previous methods
- Can extract 1,722 topics from 131 million documents
- Operates on a 28-billion-token corpus using 50 machines in 7 hours

## Abstract

Nested Chinese Restaurant Process (nCRP) topic models are powerful nonparametric Bayesian methods to extract a topic hierarchy from a given text corpus, where the hierarchical structure is automatically determined by the data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of nCRP topic models. However, hLDA has only been evaluated at small scale, because the existing collapsed Gibbs sampling and instantiated weight variational inference algorithms either are not scalable or sacrifice inference quality with mean-field assumptions. Moreover, an efficient distributed implementation of the data structures, such as dynamically growing count matrices and trees, is challenging. In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS) algorithm, which combines the advantages of collapsed and instantiated weight algorithms to achieve good scalability as well as high model quality. An initialization strategy is presented to further improve the model quality. Finally, we propose an efficient distributed implementation of PCGS through vectorization, pre-processing, and a careful design of the concurrent data structures and communication strategy. Empirical studies show that our algorithm is 111 times more efficient than the previous open-source implementation for hLDA, with comparable or even better model quality. Our distributed implementation can extract 1,722 topics from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders of magnitude larger than the previous largest corpus, with 50 machines in 7 hours.