Studying Taxonomy Enrichment on Diachronic WordNet Versions

TL;DR

This paper investigates methods for enriching and extending taxonomies like WordNet across multiple languages, focusing on resource-poor settings and providing new datasets for English and Russian.

Contribution

It introduces scalable taxonomy enrichment techniques applicable to many languages and presents novel datasets for English and Russian to evaluate these methods.

Findings

Developed new datasets for English and Russian taxonomy enrichment

Proposed methods suitable for resource-scarce language settings

Facilitated taxonomy maintenance and extension in NLP applications

Abstract

Ontologies, taxonomies, and thesauri are used in many NLP tasks. However, most studies are focused on the creation of these lexical resources rather than the maintenance of the existing ones. Thus, we address the problem of taxonomy enrichment. We explore the possibilities of taxonomy extension in a resource-poor setting and present methods which are applicable to a large number of languages. We create novel English and Russian datasets for training and evaluating taxonomy enrichment models and describe a technique of creating such datasets for other languages.