# Probabilistic Embeddings with Laplacian Graph Priors

arXiv: https://arxiv.org/abs/2204.01846 · HTML: https://mlsanity.com/paper/2204.01846/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Offline bilingual word vectors, orthogonal transformations and the inverted softmax](https://mlsanity.com/paper/1702.03859/) (2017)
- [Understanding the Origins of Bias in Word Embeddings](https://mlsanity.com/paper/1810.03611/) (2018)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018)
- [Dynamic Word Embeddings](https://mlsanity.com/paper/1702.08359/) (2017)
- [SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity](https://mlsanity.com/paper/1608.00869/) (2016)
- [Dynamic Contextualized Word Embeddings](https://mlsanity.com/paper/2010.12684/) (2020)
- [Exponential Family Embeddings](https://mlsanity.com/paper/1608.00778/) (2016)
- [How we do things with words: Analyzing text as social and cultural data](https://mlsanity.com/paper/1907.01468/) (2019)
- [What the Vec? Towards Probabilistically Grounded Embeddings](https://mlsanity.com/paper/1805.12164/) (2018)
- [Scalable Probabilistic Matrix Factorization with Graph-Based Priors](https://mlsanity.com/paper/1908.09393/) (2019)
- [Cultural Cartography with Word Embeddings](https://mlsanity.com/paper/2007.04508/) (2020)
- [Structured Embedding Models for Grouped Data](https://mlsanity.com/paper/1709.10367/) (2017)
- [Interpretable Word Embeddings via Informative Priors](https://mlsanity.com/paper/1909.01459/) (2019)

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

- [Posterior Sampling of Probabilistic Word Embeddings](https://mlsanity.com/paper/2508.02337/) (2025)

## Abstract (arXiv)

We introduce probabilistic embeddings using Laplacian priors (PELP). The proposed model enables incorporating graph side-information into static word embeddings. We theoretically show that the model unifies several previously proposed embedding methods under one umbrella. PELP generalises graph-enhanced, group, dynamic, and cross-lingual static word embeddings. PELP also enables any combination of these previous models in a straightforward fashion. Furthermore, we empirically show that our model matches the performance of previous models as special cases. In addition, we demonstrate its flexibility by applying it to the comparison of political sociolects over time. Finally, we provide code as a TensorFlow implementation enabling flexible estimation in different settings.