HyHTM: Hyperbolic Geometry based Hierarchical Topic Models
Simra Shahid Tanay Anand Nikitha Srikanth Sumit Bhatia Balaji Krishnamurthy Nikaash Puri
Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies
in a collection of documents. However, traditional HTMs often produce
hierarchies where lowerlevel topics are unrelated and not specific enough to
their higher-level topics. Additionally, these methods can be computationally
expensive. We present HyHTM - a Hyperbolic geometry based Hierarchical Topic
Models - that addresses these limitations by incorporating hierarchical
information from hyperbolic geometry to explicitly model hierarchies in topic
models. Experimental results with four baselines show that HyHTM can better
attend to parent-child relationships among topics. HyHTM produces coherent
topic hierarchies that specialise in granularity from generic higher-level
topics to specific lowerlevel topics. Further, our model is significantly
faster and leaves a much smaller memory footprint than our best-performing
baseline.We have made the source code for our algorithm publicly accessible.