SimLDA: A tool for topic model evaluation

Autor:	Taylor, Rebecca M. C., Preez, Johan A. du
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Computer Science - Information Retrieval Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in the presence of limited data. We present a novel variational message passing algorithm as applied to Latent Dirichlet Allocation (LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In situations where marginalisation leads to non-conjugate messages, we use ideas from sampling to derive approximate update equations. In cases where conjugacy holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is used. Our algorithm, ALBU (approximate LBU), has strong similarities with Variational Message Passing (VMP) (which is the message passing variant of VB). To compare the performance of the algorithms in the presence of limited data, we use data sets consisting of tweets and news groups. Using coherence measures we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets. Comment: Conference Proceedings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.09299 Zobrazit plný text záznamu View this record from Arxiv