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.
Similarly, a recent work REF addressed the alignment of an arbitrary pair of lexical resources, including machine-readable dictionaries with no structure.
A Robust Approach to Aligning Heterogeneous Lexical Resources
ACL
Computer Science