# Lex2vec: making Explainable Word Embeddings via Lexical Resources

arXiv: https://arxiv.org/abs/2103.02269 · HTML: https://mlsanity.com/paper/2103.02269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets](https://mlsanity.com/paper/1308.6242/) (2013)
- [Semantic Structure and Interpretability of Word Embeddings](https://mlsanity.com/paper/1711.00331/) (2017)
- [EVE: Explainable Vector Based Embedding Technique Using Wikipedia](https://mlsanity.com/paper/1702.06891/) (2017)

## Abstract (arXiv)

In this technical report, we propose an algorithm, called Lex2vec that exploits lexical resources to inject information into word embeddings and name the embedding dimensions by means of knowledge bases. We evaluate the optimal parameters to extract a number of informative labels that is readable and has a good coverage for the embedding dimensions.