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Studying Taxonomy Enrichment on Diachronic WordNet Versions
arXiv - CS - Computation and Language Pub Date : 2020-11-23 , DOI: arxiv-2011.11536
Irina Nikishina, Alexander Panchenko, Varvara Logacheva, Natalia Loukachevitch

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.

中文翻译:

研究历时WordNet版本上的分类学丰富度

本体,分类法和叙词表已用于许多NLP任务中。但是,大多数研究都集中在这些词汇资源的创建上,而不是对现有词汇资源的维护上。因此,我们解决了分类学丰富化的问题。我们探索在资源匮乏的环境中扩展分类法的可能性,并提出适用于多种语言的方法。我们创建了新颖的英语和俄语数据集来训练和评估分类学丰富模型,并描述了为其他语言创建此类数据集的技术。
更新日期:2020-11-25
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