Combining Heterogeneous Models for Measuring Relational Similarity

Abstract

In this work, we study the problem of mea-suring relational similarity between two word pairs (e.g., silverware:fork and clothing:shirt). Due to the large number of possible relations, we argue that it is important to combine mul-tiple models based on heterogeneous informa-tion sources. Our overall system consists of two novel general-purpose relational similar-ity models and three specific word relation models. When evaluated in the setting of a recently proposed SemEval-2012 task, our ap-proach outperforms the previous best system substantially, achieving a 54.1 % relative in-crease in Spearman’s rank correlation. 1