# Intrinsic Subspace Evaluation of Word Embedding Representations

arXiv: https://arxiv.org/abs/1606.07902 · HTML: https://mlsanity.com/paper/1606.07902/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2016.

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

- [Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2102.00894/) (2021)
- [Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages](https://mlsanity.com/paper/2203.14139/) (2022)
- [Do Language Models Know the Way to Rome?](https://mlsanity.com/paper/2109.07971/) (2021)
- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [PolyLM: Learning about Polysemy through Language Modeling](https://mlsanity.com/paper/2101.10448/) (2021)

## Abstract (arXiv)

We introduce a new methodology for intrinsic evaluation of word representations. Specifically, we identify four fundamental criteria based on the characteristics of natural language that pose difficulties to NLP systems; and develop tests that directly show whether or not representations contain the subspaces necessary to satisfy these criteria. Current intrinsic evaluations are mostly based on the overall similarity or full-space similarity of words and thus view vector representations as points. We show the limits of these point-based intrinsic evaluations. We apply our evaluation methodology to the comparison of a count vector model and several neural network models and demonstrate important properties of these models.