Studying Taxonomy Enrichment on Diachronic WordNet Versions

Irina Nikishina, Varvara Logacheva, Alexander Panchenko, Natalia Loukachevitch


Abstract
Ontologies, taxonomies, and thesauri have always been in high demand in a large number of NLP tasks. However, most studies are focused on the creation of lexical resources rather than the maintenance of the existing ones and keeping them up-to-date. In this paper, we address the problem of taxonomy enrichment. Namely, we explore the possibilities of taxonomy extension in a resource-poor setting and present several methods which are applicable to a large number of languages. We also create novel English and Russian datasets for training and evaluating taxonomy enrichment systems and describe a technique of creating such datasets for other languages.
Anthology ID:
2020.coling-main.276
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3095–3106
Language:
URL:
https://aclanthology.org/2020.coling-main.276
DOI:
10.18653/v1/2020.coling-main.276
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.276.pdf
Code
 skoltech-nlp/diachronic-wordnets