There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalization, specialization and co-hyponymy transfer). In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types. Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.

翻译：已有充分证据表明，词汇意义变化的方式可被归为不同类型的变化，这些类型突显了新旧意义之间的关系（包括泛化、特化及共下义转移）。本文提出一种通过构建模型来检测这些变化类型的方法，该模型同时利用共时词汇关系与词义定义中的信息。具体而言，我们使用WordNet中的同义词集定义与层级信息，并在Blank（1997）语义变化类型数据集的数字化版本上进行测试。最后，我们展示了词义关系如何改进模型，使其既能更准确地近似人类对语义相关性的判断，又能提升二元词汇语义变化检测的性能。