EMPIRICAL PRIOR LATENT DIRICHLET ALLOCATION MODEL
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Date
2019-01
Authors
Adegoke, M.A.
Ayeni, J. O. A.
Adewole, P.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Nigerian Journal of Technology (NIJOTECH), Faculty of Engineering, University of Nigeria, Nsukka
Abstract
In this study, empirical prior Dirichlet allocation (epLDA) model that uses latent semantic indexing framework to derive the priors required for topics computation from data is presented. The parameters of the priors so obtained are related to the parameters of the conventional LDA model using exponential function. The model was implemented and tested with benchmarked data and it achieves a prediction accuracy of 92.15%. It was observed that the epLDA model consistently outperforms the conventional LDA model on different datasets with an average percentage accuracy of 6.33%; this clearly demonstrates the advantage of using side information obtained from data for the computation of the mixture components.
Description
Keywords
Latent Dirichlet allocation , Semantic indexing , Empirical prior , Hidden structures , Prediction accuracy
Citation
Adegoke, M.A., Ayeni, J. O. A., Adewole, P.A. (2019). EMPIRICAL PRIOR LATENT DIRICHLET ALLOCATION MODEL. Nigerian Journal of Technology (NIJOTECH), Faculty of Engineering, University of Nigeria, Nsukka. 38(1)