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Lexical sense alignment using weighted bipartite b-matching

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
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).
Conference Name: 
2nd Conference on Language, Data and Knowledge (LDK 2019)
Proceedings: 
2nd Conference on Language, Data and Knowledge (LDK 2019)
Digital Object Identifer (DOI): 
NA
Publication Date: 
20/05/2019
Conference Location: 
Germany
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes