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        <title>WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration</title>
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            <namePart type="given">Eunjeong</namePart>
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            <namePart type="given">Masato</namePart>
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    <abstract>The WordNet database of English (Fellbaum, 1998) is a key source of semantic information for research and development of natural language processing applications. As the sophistication of these applications increases with the use of large datasets, deep learning, and graph-based methods, so should the use of WordNet. To this end, we introduce WAFFLE: WordNet Applied to FreeForm Linguistic Exploration which makes WordNet available in an open source graph data structure. The WAFFLE graph relies on platform agnostic formats for robust interrogation and flexibility. Where existing implementations of WordNet offer dictionary-like lookup, single degree neighborhood operations, and path based similarity-scoring, the WAFFLE graph makes all nodes (semantic relation sets) and relationships queryable at scale, enabling local and global analysis of all relationships without the need for custom code. We demonstrate WAFFLE’s ease of use, visualization capabilities, and scalable efficiency with common queries, operations, and interactions. WAFFLE is available at github.com/TRSS-NLP/WAFFLE.</abstract>
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    <identifier type="doi">10.18653/v1/2020.nlposs-1.21</identifier>
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%0 Conference Proceedings
%T WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration
%A Ekmekci, Berk
%A Howald, Blake
%Y Park, Eunjeong L.
%Y Hagiwara, Masato
%Y Milajevs, Dmitrijs
%Y Liu, Nelson F.
%Y Chauhan, Geeticka
%Y Tan, Liling
%S Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ekmekci-howald-2020-waffle
%X The WordNet database of English (Fellbaum, 1998) is a key source of semantic information for research and development of natural language processing applications. As the sophistication of these applications increases with the use of large datasets, deep learning, and graph-based methods, so should the use of WordNet. To this end, we introduce WAFFLE: WordNet Applied to FreeForm Linguistic Exploration which makes WordNet available in an open source graph data structure. The WAFFLE graph relies on platform agnostic formats for robust interrogation and flexibility. Where existing implementations of WordNet offer dictionary-like lookup, single degree neighborhood operations, and path based similarity-scoring, the WAFFLE graph makes all nodes (semantic relation sets) and relationships queryable at scale, enabling local and global analysis of all relationships without the need for custom code. We demonstrate WAFFLE’s ease of use, visualization capabilities, and scalable efficiency with common queries, operations, and interactions. WAFFLE is available at github.com/TRSS-NLP/WAFFLE.
%R 10.18653/v1/2020.nlposs-1.21
%U https://aclanthology.org/2020.nlposs-1.21
%U https://doi.org/10.18653/v1/2020.nlposs-1.21
%P 147-157

Markdown (Informal)

[WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration](https://aclanthology.org/2020.nlposs-1.21) (Ekmekci & Howald, NLPOSS 2020)

• WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration (Ekmekci & Howald, NLPOSS 2020)

ACL

• Berk Ekmekci and Blake Howald. 2020. WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration. In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS), pages 147–157, Online. Association for Computational Linguistics.