# A Visual Distance for WordNet

Source: [A Visual Distance for WordNet](<https://www.sota2.com/research/paper/68fa644d1e209131cea03dea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:01:12.135Z

Authors: Raquel P\\'erez-Arnal, Armand Vilalta, Dario Garcia-Gasulla, Ulises Cort\\'es, Eduard Ayguad\\'e, Jesus Labarta

Published: 2018-04-24T15:34:33.000Z

## Abstract

Measuring the distance between concepts is an important field of study of Natural Language Processing, as it can be used to improve tasks related to the interpretation of those same concepts. WordNet, which includes a wide variety of concepts associated with words (i.e., synsets), is often used as a source for computing those distances. In this paper, we explore a distance for WordNet synsets based on visual features, instead of lexical ones. For this purpose, we extract the graphic features generated within a deep convolutional neural networks trained with ImageNet and use those features to generate a representative of each synset. Based on those representatives, we define a distance measure of synsets, which complements the traditional lexical distances. Finally, we propose some experiments to evaluate its performance and compare it with the current state-of-the-art.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1804.09558>)

[PDF](<https://arxiv.org/pdf/1804.09558.pdf>)