Lexical Sense Alignment using Weighted Bipartite b-Matching
Sina Ahmadi, Mihael Arčan, John Philip McCrae · Arrow@dit (Dublin Institute of Technology) · 2019
In this study, we present a similarity-based approach for lexical sense alignment in WordNet and Wiktionary with a focus on the polysemous items. Our approach relies on semantic textual similarity using features such as string distance metrics and word embeddings, and a graph matching algorithm. Transforming the alignment problem into a bipartite graph matching enables us to apply graph matching algorithms, in particular, weighted bipartite b-matching (WBbM).