# Word Embeddings from Large-Scale Greek Web Content

arXiv: https://arxiv.org/abs/1810.06694 · HTML: https://mlsanity.com/paper/1810.06694/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [GreekBART: The First Pretrained Greek Sequence-to-Sequence Model](https://mlsanity.com/paper/2304.00869/) (2023)
- [NLP for The Greek Language: A Longer Survey](https://mlsanity.com/paper/2408.10962/) (2024)
- [GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization](https://mlsanity.com/paper/2311.07767/) (2023)
- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)
- [NLP Research and Resources at DaSciM, Ecole Polytechnique](https://mlsanity.com/paper/2112.00566/) (2021)
- [A Greek Government Decisions Dataset for Public-Sector Analysis and Insight](https://mlsanity.com/paper/2512.05647/) (2025)
- [MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation](https://mlsanity.com/paper/2602.21379/) (2026)

## Abstract (arXiv)

Word embeddings are undoubtedly very useful components in many NLP tasks. In this paper, we present word embeddings and other linguistic resources trained on the largest to date digital Greek language corpus. We also present a live web tool for testing the Greek word embeddings, by offering "analogy", "similarity score" and "most similar words" functions. Through our explorer, one could interact with the Greek word vectors.