Lexical sense alignment using weighted bipartite b-matching
Ahmadi, Sina ; Arcan, Mihael ; McCrae, John
Ahmadi, Sina
Arcan, Mihael
McCrae, John
Loading...
Repository DOI
Publication Date
2019-05-20
Type
Conference Poster
Downloads
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
Publisher
NUI Galway
Publisher DOI
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland