# Spherical Text Embedding

arXiv: https://arxiv.org/abs/1911.01196 · HTML: https://mlsanity.com/paper/1911.01196/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 30 of 49)

- [RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry](https://mlsanity.com/paper/2502.03251/) (2025)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [Pseudo-Riemannian Graph Convolutional Networks](https://mlsanity.com/paper/2106.03134/) (2021)
- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach](https://mlsanity.com/paper/2106.04941/) (2021)
- [SphereFace Revived: Unifying Hyperspherical Face Recognition](https://mlsanity.com/paper/2109.05565/) (2021)
- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction](https://mlsanity.com/paper/2306.11443/) (2023)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs](https://mlsanity.com/paper/2209.08767/) (2022)
- [Linear Classifiers in Product Space Forms](https://mlsanity.com/paper/2102.10204/) (2021)
- [Vacaspati: A Diverse Corpus of Bangla Literature](https://mlsanity.com/paper/2307.05083/) (2023)
- [MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information](https://mlsanity.com/paper/2111.04022/) (2021)
- [Switch Spaces: Learning Product Spaces with Sparse Gating](https://mlsanity.com/paper/2102.08688/) (2021)
- [On Hyperbolic Embeddings in 2D Object Detection](https://mlsanity.com/paper/2203.08049/) (2022)
- [Text Transformations in Contrastive Self-Supervised Learning: A Review](https://mlsanity.com/paper/2203.12000/) (2022)
- [Principal Component Analysis in Space Forms](https://mlsanity.com/paper/2301.02750/) (2023)
- [AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System](https://mlsanity.com/paper/2203.14683/) (2022)
- [Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold](https://mlsanity.com/paper/2307.04514/) (2023)
- [Topological Obstructions and How to Avoid Them](https://mlsanity.com/paper/2312.07529/) (2023)
- [Analysis and Prediction of NLP Models Via Task Embeddings](https://mlsanity.com/paper/2112.05647/) (2021)
- [ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction](https://mlsanity.com/paper/2203.06591/) (2022)
- [Dynamic Bundling with Large Language Models for Zero-Shot Inference on Text-Attributed Graphs](https://mlsanity.com/paper/2505.17599/) (2025)
- [Automatic Generation of Multiple-Choice Questions](https://mlsanity.com/paper/2303.14576/) (2023)
- [FMGNN: Fused Manifold Graph Neural Network](https://mlsanity.com/paper/2304.01081/) (2023)
- [Normed Spaces for Graph Embedding](https://mlsanity.com/paper/2312.01502/) (2023)
- [Directional Textual Inversion for Personalized Text-to-Image Generation](https://mlsanity.com/paper/2512.13672/) (2025)
- [Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis](https://mlsanity.com/paper/2106.14431/) (2021)
- [Kernel distance measures for time series, random fields and other structured data](https://mlsanity.com/paper/2109.14752/) (2021)

## Abstract (arXiv)

Unsupervised text embedding has shown great power in a wide range of NLP tasks. While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding. To close this gap, we propose a spherical generative model based on which unsupervised word and paragraph embeddings are jointly learned. To learn text embeddings in the spherical space, we develop an efficient optimization algorithm with convergence guarantee based on Riemannian optimization. Our model enjoys high efficiency and achieves state-of-the-art performances on various text embedding tasks including word similarity and document clustering.