# Referential Uncertainty and Word Learning in High-dimensional, Continuous Meaning Spaces

arXiv: https://arxiv.org/abs/1609.09580 · HTML: https://mlsanity.com/paper/1609.09580/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2016.

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

- [Emergent Communication in Continuous Worlds: Self-Organisation of Conceptually Grounded Vocabularies at Scale](https://mlsanity.com/paper/2401.08461/) (2024)

## Abstract (arXiv)

This paper discusses lexicon word learning in high-dimensional meaning spaces from the viewpoint of referential uncertainty. We investigate various state-of-the-art Machine Learning algorithms and discuss the impact of scaling, representation and meaning space structure. We demonstrate that current Machine Learning techniques successfully deal with high-dimensional meaning spaces. In particular, we show that exponentially increasing dimensions linearly impact learner performance and that referential uncertainty from word sensitivity has no impact.