# Comparative Analysis of Word Embeddings for Capturing Word Similarities

arXiv: https://arxiv.org/abs/2005.03812 · HTML: https://mlsanity.com/paper/2005.03812/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 9 of 9)

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- [Comparing in context: Improving cosine similarity measures with a metric tensor](https://mlsanity.com/paper/2203.14996/) (2022)
- [Using Person Embedding to Enrich Features and Data Augmentation for Classification](https://mlsanity.com/paper/2206.15162/) (2022)
- [Simbanex: Similarity-based Exploration of IEEE VIS Publications](https://mlsanity.com/paper/2409.00478/) (2024)
- [VLCE: A Knowledge-Enhanced Framework for Image Description in Disaster Assessment](https://mlsanity.com/paper/2509.21609/) (2025)

## Abstract (arXiv)

Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.