Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams,
Streaming Variational Bayes
(SVB) introduced
Bayesian updating
to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored three challenges of processing real-world streams:
topic evolution
,
data turbulence
, and
real-time inference
. In this article, we propose a novel distributed LDA algorithm—referred to as
StreamFed-LDA—
to deal with challenges on streams. For topic modeling of streaming data, the ability to capture evolving topics is essential for practical online inference. To achieve this goal,
StreamFed-LDA
is based on a specialized framework that supports lifelong (continual) learning of evolving topics. On the other hand, data turbulence is commonly present in streams due to real-life events. In that case, the design of
StreamFed-LDA
allows the model to learn new characteristics from the most recent data while maintaining the historical information. On massive streaming data, it is difficult and crucial to provide real-time inference results. To increase the throughput and reduce the latency,
StreamFed-LDA
introduces additional techniques that substantially reduce both computation and communication costs in distributed systems. Experiments on four real-world datasets show that the proposed framework achieves significantly better performance of online inference compared with the baselines. At the same time,
StreamFed-LDA
also reduces the latency by orders of magnitudes in real-world datasets.
Language model adaptation using latent dirichlet allocation and an efficient topic inference algorithm