# Nonparametric Spherical Topic Modeling with Word Embeddings

arXiv: https://arxiv.org/abs/1604.00126 · HTML: https://mlsanity.com/paper/1604.00126/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2016.

## Built on by (most cited first, 16 of 16)

- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [Prompting Large Language Models for Topic Modeling](https://mlsanity.com/paper/2312.09693/) (2023)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds](https://mlsanity.com/paper/2205.01845/) (2022)
- [Text Transformations in Contrastive Self-Supervised Learning: A Review](https://mlsanity.com/paper/2203.12000/) (2022)
- [KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation](https://mlsanity.com/paper/2307.01878/) (2023)
- [Connecting Sphere Manifolds Hierarchically for Regularization](https://mlsanity.com/paper/2106.13549/) (2021)
- [No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling](https://mlsanity.com/paper/2208.01712/) (2022)
- [Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis](https://mlsanity.com/paper/2106.14431/) (2021)
- [Variational Language Concepts for Interpreting Foundation Language Models](https://mlsanity.com/paper/2410.03964/) (2024)
- [S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling](https://mlsanity.com/paper/2507.12451/) (2025)
- [Semiparametric Latent Topic Modeling on Consumer-Generated Corpora](https://mlsanity.com/paper/2107.10651/) (2021)
- [Generalised Spherical Text Embedding](https://mlsanity.com/paper/2211.16801/) (2022)
- [Embedded Topic Models Enhanced by Wikification](https://mlsanity.com/paper/2410.02441/) (2024)
- [A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance](https://mlsanity.com/paper/2609.16592/) (2026)

## Abstract (arXiv)

Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.