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:
Cite (ACL):
Irina Nikishina, Varvara Logacheva, Alexander Panchenko, and Natalia Loukachevitch. 2020. Studying Taxonomy Enrichment on Diachronic WordNet Versions. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3095–3106, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Studying Taxonomy Enrichment on Diachronic WordNet Versions (Nikishina et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.276.pdf
Code
 skoltech-nlp/diachronic-wordnets