# Learning Geometric Word Meta-Embeddings

arXiv: https://arxiv.org/abs/2004.09219 · HTML: https://mlsanity.com/paper/2004.09219/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

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

- [On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry](https://mlsanity.com/paper/2106.00286/) (2021)
- [A Survey on Word Meta-Embedding Learning](https://mlsanity.com/paper/2204.11660/) (2022)
- [Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings](https://mlsanity.com/paper/2204.12386/) (2022)
- [Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models](https://mlsanity.com/paper/2204.07746/) (2022)
- [A Meta-embedding-based Ensemble Approach for ICD Coding Prediction](https://mlsanity.com/paper/2102.13622/) (2021)
- [Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings](https://mlsanity.com/paper/2305.19092/) (2023)

## Abstract (arXiv)

We propose a geometric framework for learning meta-embeddings of words from different embedding sources. Our framework transforms the embeddings into a common latent space, where, for example, simple averaging of different embeddings (of a given word) is more amenable. The proposed latent space arises from two particular geometric transformations - the orthogonal rotations and the Mahalanobis metric scaling. Empirical results on several word similarity and word analogy benchmarks illustrate the efficacy of the proposed framework.