# Linking GloVe with word2vec

arXiv: https://arxiv.org/abs/1411.5595 · HTML: https://mlsanity.com/paper/1411.5595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2014.

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

- [Efficient Toxicity Detection in Gaming Chats: A Comparative Study of Embeddings, Fine-Tuned Transformers and LLMs](https://mlsanity.com/paper/2510.17924/) (2025)

## Abstract (arXiv)

The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. is reported to be an efficient and effective method for learning vector representations of words. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec tool. In this note, we explain the similarities between the training objectives of the two models, and show that the objective of SGNS is similar to the objective of a specialized form of GloVe, though their cost functions are defined differently.