# Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources

arXiv: https://arxiv.org/abs/2005.10048 · HTML: https://mlsanity.com/paper/2005.10048/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

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

- [Domain Lexical Knowledge-based Word Embedding Learning for Text Classification under Small Data](https://mlsanity.com/paper/2506.01621/) (2025)

## Abstract (arXiv)

In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.