A Robust Approach to Aligning Heterogeneous Lexical Resources

Lexical resource alignment has been an active field of research over the last decade. However, prior methods for aligning lexical resources have been either specific to a particular pair of resources, or heavily dependent on the availability of hand-crafted alignment data for the pair of resources to be aligned. Here we present a unified approach that can be applied to an arbitrary pair of lexical resources, including machine-readable dictionaries with no network structure. Our approach leverages a similarity measure that enables the structural comparison of senses across lexical resources, achieving state-of-the-art performance on the task of aligning WordNet to three different collaborative resources: Wikipedia, Wiktionary and OmegaWiki.