# Polylingual Wordnet

## TL;DR

This paper presents an automatic translation method for expanding WordNet to multiple languages by leveraging existing translations and contextual information from parallel corpora, improving over previous approaches.

## Contribution

It introduces a novel approach that uses contextual information from parallel corpora and pivot languages to enhance multilingual WordNet translation accuracy.

## Key findings

- Significant improvement over non-contextual translation methods
- Effective use of pivot languages impacts disambiguation performance
- Validated on 10 European language WordNets

## Abstract

Princeton WordNet is one of the most important resources for natural language processing, but is only available for English. While it has been translated using the expand approach to many other languages, this is an expensive manual process. Therefore it would be beneficial to have a high-quality automatic translation approach that would support NLP techniques, which rely on WordNet in new languages. The translation of wordnets is fundamentally complex because of the need to translate all senses of a word including low frequency senses, which is very challenging for current machine translation approaches. For this reason we leverage existing translations of WordNet in other languages to identify contextual information for wordnet senses from a large set of generic parallel corpora. We evaluate our approach using 10 translated wordnets for European languages. Our experiment shows a significant improvement over translation without any contextual information. Furthermore, we evaluate how the choice of pivot languages affects performance of multilingual word sense disambiguation.