Exploring graph-based representations for taxonomy enrichment

Irina Nikishina, Natalia Loukachevitch, Varvara Logacheva, Alexander Panchenko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationGWC 2021 - Proceedings of the 11th Global Wordnet Conference
EditorsSonja Bosch, Christiane Fellbaum, Marissa Griesel, Alexandre Rademaker, Piek Vossen
PublisherGlobal WordNet Association
Number of pages11
ISBN (Electronic)9789464027310
Publication statusPublished - 2021
Event11th Global Wordnet Conference, GWC 2021 - Potchefstroom, South Africa
Duration: 18 Jan 202121 Jan 2021

Publication series

NameGWC 2021 - Proceedings of the 11th Global Wordnet Conference


Conference11th Global Wordnet Conference, GWC 2021
Country/TerritorySouth Africa


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