Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions

Irina Nikishina, Natalia Loukachevitch, Varvara Logacheva, Alexander Panchenko


Abstract
The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy. On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures. Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations. In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings. We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.
Anthology ID:
2021.gwc-1.15
Volume:
Proceedings of the 11th Global Wordnet Conference
Month:
January
Year:
2021
Address:
University of South Africa (UNISA)
Venues:
EACL | GWC
SIG:
Publisher:
Global Wordnet Association
Note:
Pages:
126–136
Language:
URL:
https://aclanthology.org/2021.gwc-1.15
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.gwc-1.15.pdf
Data
SemEval-2018 Task 9: Hypernym Discovery