We offer a Java implementation and library for Topic Grouper, a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. It is governed by a simple generative model, where the likelihood to generate the training documents via topics is optimized. The algorithm starts with one-word topics and joins two topics at every step. It therefore generates a solution for every desired number of topics ranging between the size of the training vocabulary and one. The process represents an agglomerative clustering that corresponds to a binary tree of topics. A resulting tree may act as a containment hierarchy, typically with more general topics towards the root of tree and more specific topics towards the leaves. Topic Grouper is not governed by a background distribution such as the Dirichlet and avoids hyper parameter optimizations.
This is the publication behind the appoach: https://link.springer.com/chapter/10.1007/978-3-030-15712-8_38
Also, there is an extended version of this publication available on ArXiv: http://arxiv.org/abs/1904.06483
The related conference slides can be found here: https://github.com/pfeiferd/TopicGrouperJ/blob/master/TopicGrouperECIR2019.pdf