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.

使用基于双曲几何的层级主题模型（HyHTM）可以更好地对主题进行建模，使得特定的低级主题与相应的高级主题关联更紧密。同时，该模型也具备更高的计算效率和更小的内存占用。