# Structured Embedding Models for Grouped Data

arXiv: https://arxiv.org/abs/1709.10367 · HTML: https://mlsanity.com/paper/1709.10367/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora](https://mlsanity.com/paper/2112.14330/) (2021)
- [Detecting Anomalies within Time Series using Local Neural Transformations](https://mlsanity.com/paper/2202.03944/) (2022)
- [Probabilistic Embeddings with Laplacian Graph Priors](https://mlsanity.com/paper/2204.01846/) (2022)
- [Torch-Choice: A PyTorch Package for Large-Scale Choice Modeling with Python](https://mlsanity.com/paper/2304.01906/) (2023)
- [Harnessing Geometric Constraints from Emotion Labels to improve Face Verification](https://mlsanity.com/paper/2103.03862/) (2021)
- [Posterior Sampling of Probabilistic Word Embeddings](https://mlsanity.com/paper/2508.02337/) (2025)

## Abstract (arXiv)

Word embeddings are a powerful approach for analyzing language, and exponential family embeddings (EFE) extend them to other types of data. Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data. We study how the word usage of U.S. Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons. Key to the success of our method is that the groups share statistical information. We develop two sharing strategies: hierarchical modeling and amortization. We demonstrate the benefits of this approach in empirical studies of speeches, abstracts, and shopping baskets. We show how S-EFE enables group-specific interpretation of word usage, and outperforms EFE in predicting held-out data.