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Lexical sense alignment using weighted bipartite b-matching

Ahmadi, Sina
; 
Arcan, Mihael
; 
McCrae, John
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Publication Date
2019-05-20
Keywords
Lexical sense alignment, resource alignment, e-lexicography, graph matching
Type
Conference Poster
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Citation
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.
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).
Funder
Horizon 2020
Publisher
NUI Galway
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Data Science Institute (Conference Posters)