# Correlation-based Intrinsic Evaluation of Word Vector Representations

arXiv: https://arxiv.org/abs/1606.06710 · HTML: https://mlsanity.com/paper/1606.06710/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations](https://mlsanity.com/paper/2203.07511/) (2022)
- [VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models](https://mlsanity.com/paper/2203.07504/) (2022)
- [Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models](https://mlsanity.com/paper/2305.14307/) (2023)
- [SuperSim: a test set for word similarity and relatedness in Swedish](https://mlsanity.com/paper/2104.05228/) (2021)

## Abstract (arXiv)

We introduce QVEC-CCA--an intrinsic evaluation metric for word vector representations based on correlations of learned vectors with features extracted from linguistic resources. We show that QVEC-CCA scores are an effective proxy for a range of extrinsic semantic and syntactic tasks. We also show that the proposed evaluation obtains higher and more consistent correlations with downstream tasks, compared to existing approaches to intrinsic evaluation of word vectors that are based on word similarity.