Studying Taxonomy Enrichment on Diachronic WordNet Versions

Proceedings of the 28th International Conference on Computational Linguistics, pages 3095–3106 Barcelona, Spain (Online), December 8-13, 2020 3095 Studying Taxonomy Enrichment on Diachronic WordNet Versions Irina Nikishina‡, Alexander Panchenko‡, Varvara Logacheva‡, and Natalia Loukachevitch† ‡Skolkovo Institute of Science and Technology, Moscow, Russia †Research Computing Center, Lomonosov Moscow State University, Moscow, Russia {Irina.Nikishina,A.Panchenko,V.Logacheva}@skoltech.ru louk nat@mail.ru Abstract Ontologies, taxonomies, and thesauri are used in many NLP tasks. However, most studies are focused on the creation of these lexical resources rather than the maintenance of the existing ones. Thus, we address the problem of taxonomy enrichment. We explore the possibilities of taxonomy extension in a resource-poor setting and present methods which are applicable to a large number of languages. We create novel English and Russian datasets for training and evaluating taxonomy enrichment models and describe a technique of creating such datasets for other languages. 1 Introduction Nowadays, construction and maintenance of lexical resources (ontologies, knowledge bases, thesauri) have become essential for the NLP community. In particular, enriching the most acknowledged lexi- cal databases like WordNet (Miller, 1998) and its variants for almost 50 languages1 or collaboratively created lexical resources such as Wiktionary is crucial. Resources of this kind are widely used in mul- tiple NLP tasks: Word Sense Disambiguation, Entity Linking (Moro and Navigli, 2015), Named Entity Recognition, Coreference Resolution (Ponzetto and Strube, 2006). There already exist several initiatives on WordNet extension, for example, the Open English WordNet with thousands of new manually added entries or plWordNet (Maziarz et al., 2014) which includes a mapping to an enlarged Princeton WordNet. However, the manual annotation process is too costly: it is time-consuming and requires language or domain experts. On the other hand, automatically created datasets and resources usually lag in quality compared to manually labelled ones. Therefore, it would be beneficial to assist manual work by introducing automatic annotation systems to keep valuable lexical resources up-to-date. In this paper, we analyse the approaches to automatic enrichment of wordnets. Formally, the goal of the Taxonomy Enrichment task is as follows: given words that are not included in a taxonomy (hereinafter orphan words), we need to associate each word with the appropriate hypernyms from it. For example, given an input word “duck” we need to provide a list of the most probable hyper- nyms the word could be attached to, e.g. “waterfowl”, “bird”. A word may have multiple hypernyms. SemEval-2016 task 14 (Jurgens and Pilehvar, 2016) was the first effort to evaluate this task in a con- trolled environment, but it provided an unrealistic task scenario. Namely, the participants were given definitions of words to be added to taxonomy, which are often unavailable in real world. The majority of presented methods heavily depended on these definition. In contrast to that, we present a resource-poor scenario and solutions which conform to it. Our contributions are as follows: • We create datasets for studying diachronic evolution of wordnets for English and Russian, extending the monolingual setup of the RUSSE’2020 shared task (Nikishina et al., 2020) with a larger …

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