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：大量证据表明，词语意义演变的方式可按新旧意义间的关系（包括广义化、狭义化及同下位义转移等类型）进行分类。本文提出一种检测这些演变类型的方法，通过构建一个同时利用共时词汇关系与词义定义信息的模型来实现。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）语义演变类型数据集的数字化版本上进行测试。最后，我们展示了语义关系如何优化两种模型：一是对人类语义关联性判断的近似建模，二是二元词汇语义演变检测。