# Text comparison using word vector representations and dimensionality reduction

arXiv: https://arxiv.org/abs/1607.00534 · HTML: https://mlsanity.com/paper/1607.00534/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2016.

## Built on by (most cited first, 2 of 2)

- [Current Topological and Machine Learning Applications for Bias Detection in Text](https://mlsanity.com/paper/2311.13495/) (2023)
- [Building A Knowledge Graph to Enrich ChatGPT Responses in Manufacturing Service Discovery](https://mlsanity.com/paper/2404.06571/) (2024)

## Abstract (arXiv)

This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (t-SNE) and how it can be implemented using Python. The technique provides a bird's-eye view of text sources, e.g. text summaries and their source material, and enables users to explore text sources like a geographical map. Word vector representations capture many linguistic properties such as gender, tense, plurality and even semantic concepts like "capital city of". Using dimensionality reduction, a 2D map can be computed where semantically similar words are close to each other. The technique uses the word2vec model from the gensim Python library and t-SNE from scikit-learn.