# Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations

arXiv: https://arxiv.org/abs/1611.01547 · HTML: https://mlsanity.com/paper/1611.01547/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [Large Scale Substitution-based Word Sense Induction](https://mlsanity.com/paper/2110.07681/) (2021)
- [What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies](https://mlsanity.com/paper/2310.14793/) (2023)
- [Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings](https://mlsanity.com/paper/2403.16984/) (2024)

## Abstract (arXiv)

We propose a language-agnostic way of automatically generating sets of semantically similar clusters of entities along with sets of "outlier" elements, which may then be used to perform an intrinsic evaluation of word embeddings in the outlier detection task. We used our methodology to create a gold-standard dataset, which we call WikiSem500, and evaluated multiple state-of-the-art embeddings. The results show a correlation between performance on this dataset and performance on sentiment analysis.