Lex2vec: making Explainable Word Embeddings via Lexical Resources
Not on the map. Why?
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 3 Mar 2021.
Builds on
4 of 21 references.
References · 4
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2013NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweetscited by 33
- 2017Semantic Structure and Interpretability of Word Embeddingscited by 21
- 2017EVE: Explainable Vector Based Embedding Technique Using Wikipediacited by 6
Built on by
Not cited yet.