Lexical Sense Alignment using Weighted Bipartite b-Matching

Sina Ahmadi

doi:http://hdl.handle.net/10379/15214

Lexical Sense Alignment using Weighted Bipartite b-Matching

Sina Ahmadi

Computer Science

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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).

Original language	English (Ireland)
Title of host publication	2nd Conference on Language, Data and Knowledge (LDK 2019)
Place of Publication	Leipzig, Germany
DOIs	https://doi.org/http://hdl.handle.net/10379/15214
Publication status	Published - 1 May 2019

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Ahmadi, Sina; Arcan, Mihael; McCrae, John

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http://hdl.handle.net/10379/15214

Cite this

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title = "Lexical Sense Alignment using Weighted Bipartite b-Matching",

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AB - 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).

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M3 - Conference Publication

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ER -

Lexical Sense Alignment using Weighted Bipartite b-Matching

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this