论文标题
研究对历时WordNet版本的分类学富集
Studying Taxonomy Enrichment on Diachronic WordNet Versions
论文作者
论文摘要
许多NLP任务都使用了本体，分类法和词库。但是，大多数研究都集中在创建这些词汇资源上，而不是维持现有资源。因此，我们解决了分类富集的问题。我们在资源贫乏的设置和当前的方法中探讨了分类扩展的可能性，这些方法适用于大量语言。我们创建了新颖的英语和俄罗斯数据集来培训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。
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.
