Irina Nikishina


2021

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Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina | Natalia Loukachevitch | Varvara Logacheva | Alexander Panchenko
Proceedings of the 11th Global Wordnet Conference

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.

2020

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Studying Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina | Varvara Logacheva | Alexander Panchenko | Natalia Loukachevitch
Proceedings of the 28th International Conference on Computational Linguistics

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.