Studying Taxonomy Enrichment on Diachronic WordNet Versions
This work is significant for researchers and practitioners working with lexical resources in NLP, particularly for maintaining and extending taxonomies in languages with limited resources.
This paper addresses the problem of taxonomy enrichment, specifically focusing on extending existing taxonomies in resource-poor settings. The authors created novel English and Russian datasets for training and evaluating taxonomy enrichment models and developed a technique for generating such datasets for other languages.
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.