# Stochastic Divergence Minimization for Biterm Topic Model

## TL;DR

This paper introduces a stochastic divergence minimization algorithm for the Biterm Topic Model, improving the accuracy and scalability of short text topic inference compared to existing methods.

## Contribution

It proposes a novel stochastic inference algorithm for BTM that reduces computational complexity and enhances estimation accuracy over prior approaches.

## Key findings

- The new algorithm outperforms existing inference methods in experiments.
- It achieves better scalability for large short text datasets.
- Demonstrates improved accuracy in latent topic estimation.

## Abstract

As the emergence and the thriving development of social networks, a huge number of short texts are accumulated and need to be processed. Inferring latent topics of collected short texts is useful for understanding its hidden structure and predicting new contents. Unlike conventional topic models such as latent Dirichlet allocation (LDA), a biterm topic model (BTM) was recently proposed for short texts to overcome the sparseness of document-level word co-occurrences by directly modeling the generation process of word pairs. Stochastic inference algorithms based on collapsed Gibbs sampling (CGS) and collapsed variational inference have been proposed for BTM. However, they either require large computational complexity, or rely on very crude estimation. In this work, we develop a stochastic divergence minimization inference algorithm for BTM to estimate latent topics more accurately in a scalable way. Experiments demonstrate the superiority of our proposed algorithm compared with existing inference algorithms.