jLDADMM: A Java package for the LDA and DMM topic models
This is an incremental technical contribution providing a new software package for researchers and practitioners in natural language processing.
The authors introduced jLDADMM, a Java toolkit implementing Latent Dirichlet Allocation and Dirichlet Multinomial Mixture topic models using collapsed Gibbs sampling, providing an alternative for topic modeling on normal or short texts with an open-source release.
In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. jLDADMM is open-source and available to download at: https://github.com/datquocnguyen/jLDADMM