# HyHTM: Hyperbolic Geometry based Hierarchical Topic Models

arXiv: https://arxiv.org/abs/2305.09258 · HTML: https://mlsanity.com/paper/2305.09258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 17 of 17)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Low-Dimensional Hyperbolic Knowledge Graph Embeddings](https://mlsanity.com/paper/2005.00545/) (2020)
- [Poincar'e GloVe: Hyperbolic Word Embeddings](https://mlsanity.com/paper/1810.06546/) (2018)
- [CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data](https://mlsanity.com/paper/2111.15037/) (2021)
- [Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network](https://mlsanity.com/paper/2107.02757/) (2021)
- [Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding](https://mlsanity.com/paper/2007.09536/) (2020)
- [HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding](https://mlsanity.com/paper/2210.10625/) (2022)
- [APo-VAE: Text Generation in Hyperbolic Space](https://mlsanity.com/paper/2005.00054/) (2020)
- [TopicNet: Semantic Graph-Guided Topic Discovery](https://mlsanity.com/paper/2110.14286/) (2021)
- [Knowledge-Aware Bayesian Deep Topic Model](https://mlsanity.com/paper/2209.14228/) (2022)
- [A Nested HDP for Hierarchical Topic Models](https://mlsanity.com/paper/1301.3570/) (2013)
- [Nested Hierarchical Dirichlet Processes for Multi-Level Non-Parametric Admixture Modeling](https://mlsanity.com/paper/1508.06446/) (2015)

## Built on by (most cited first, 1 of 1)

- [CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling](https://mlsanity.com/paper/2604.14489/) (2026)

## Abstract (arXiv)

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.