Kentaro Kanada


Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations
Kentaro Kanada | Tetsunori Kobayashi | Yoshihiko Hayashi
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications

This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by “sense representations” (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.