Combining Heterogeneous Models for Measuring Relational Similarity

Alisa Zhila, Wen-tau Yih, Chris Meek, Geoffrey Zweig and Tomas Mikolov

In this work, we study the problem of measuring 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 multiple models based on heterogeneous information sources. Our overall system consists of two novel general-purpose relational similarity models and three specific word relation models. When evaluated in the setting of a recently proposed SemEval-2012 task, our approach outperforms the previous best system substantially, achieving a 54.1% relative increase in Spearman's rank correlation.

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