# Non-distributional Word Vector Representations

arXiv: https://arxiv.org/abs/1506.05230 · HTML: https://mlsanity.com/paper/1506.05230/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2015.

## Built on by (most cited first, 1 of 1)

- [RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)](https://mlsanity.com/paper/2105.13266/) (2021)

## Abstract (arXiv)

Data-driven representation learning for words is a technique of central importance in NLP. While indisputably useful as a source of features in downstream tasks, such vectors tend to consist of uninterpretable components whose relationship to the categories of traditional lexical semantic theories is tenuous at best. We present a method for constructing interpretable word vectors from hand-crafted linguistic resources like WordNet, FrameNet etc. These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We analyze their performance on state-of-the-art evaluation methods for distributional models of word vectors and find they are competitive to standard distributional approaches.