# Encoding Prior Knowledge with Eigenword Embeddings

arXiv: https://arxiv.org/abs/1509.01007 · HTML: https://mlsanity.com/paper/1509.01007/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2015.

## Built on by (most cited first, 2 of 2)

- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [Iterative Multilingual Spectral Attribute Erasure](https://mlsanity.com/paper/2506.11244/) (2025)

## Abstract (arXiv)

Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets.