Paper: Combining Heterogeneous Models for Measuring Relational Similarity

ACL ID N13-1120
Title Combining Heterogeneous Models for Measuring Relational Similarity
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

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.