# WordNet2Vec: Corpora Agnostic Word Vectorization Method

arXiv: https://arxiv.org/abs/1606.03335 · HTML: https://mlsanity.com/paper/1606.03335/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2016.

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

- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [Neural Embedding: Learning the Embedding of the Manifold of Physics Data](https://mlsanity.com/paper/2208.05484/) (2022)

## Abstract (arXiv)

A complex nature of big data resources demands new methods for structuring especially for textual content. WordNet is a good knowledge source for comprehensive abstraction of natural language as its good implementations exist for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism WordNet2Vec is proposed in the paper. It creates vectors for each word from WordNet. These vectors encapsulate general position - role of a given word towards all other words in the natural language. Any list or set of such vectors contains knowledge about the context of its component within the whole language. Such word representation can be easily applied to many analytic tasks like classification or clustering. The usefulness of the WordNet2Vec method was demonstrated in sentiment analysis, i.e. classification with transfer learning for the real Amazon opinion textual dataset.