Lexical sense alignment using weighted bipartite b-matching
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Ahmadi, Sina, Arcan, Mihael, & McCrae, John. (2019). Lexical Sense Alignment using Weighted Bipartite b-Matching. Poster presented at the 2nd Conference on Language, Data and Knowledge (LDK 2019) Leipzig, Germany, 20-23 May.
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).