jLDADMM: A Java package for the LDA and DMM topic models
Information Retrieval
2018-08-14 v1 Computation and Language
Machine Learning
Machine Learning
Abstract
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
Keywords
Cite
@article{arxiv.1808.03835,
title = {jLDADMM: A Java package for the LDA and DMM topic models},
author = {Dat Quoc Nguyen},
journal= {arXiv preprint arXiv:1808.03835},
year = {2018}
}