Matching Results of Latent Dirichlet Allocation for Text
Andreas Niekler, Patrick Jähnichen · PsycEXTRA Dataset · 2012
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be updated in an on-line manner. This includes inferring new documents into the model, passing parameter priors to the inference algorithm or a mixture of both, leading to more complicated and compu-tationally expensive models. We present a method to match and compare the resulting LDA topics of different models with light weight easy to use similarity measures. We address the on-line problem by keeping the model inference simple and matching topics solely by their high probability word lists.