# Extending and Improving Wordnet via Unsupervised Word Embeddings

arXiv: https://arxiv.org/abs/1705.00217 · HTML: https://mlsanity.com/paper/1705.00217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2017.

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

- [Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models](https://mlsanity.com/paper/2605.06640/) (2026)

## Abstract (arXiv)

This work presents an unsupervised approach for improving WordNet that builds upon recent advances in document and sense representation via distributional semantics. We apply our methods to construct Wordnets in French and Russian, languages which both lack good manual constructions.1 These are evaluated on two new 600-word test sets for word-to-synset matching and found to improve greatly upon synset recall, outperforming the best automated Wordnets in F-score. Our methods require very few linguistic resources, thus being applicable for Wordnet construction in low-resources languages, and may further be applied to sense clustering and other Wordnet improvements.