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        <title>Latent semantic network induction in the context of linked example senses</title>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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        <name type="personal">
            <namePart type="given">Tim</namePart>
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    <abstract>The Princeton WordNet is a powerful tool for studying language and developing natural language processing algorithms. With significant work developing it further, one line considers its extension through aligning its expert-annotated structure with other lexical resources. In contrast, this work explores a completely data-driven approach to network construction, forming a wordnet using the entirety of the open-source, noisy, user-annotated dictionary, Wiktionary. Comparing baselines to WordNet, we find compelling evidence that our network induction process constructs a network with useful semantic structure. With thousands of semantically-linked examples that demonstrate sense usage from basic lemmas to multiword expressions (MWEs), we believe this work motivates future research.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5523</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Latent semantic network induction in the context of linked example senses
%A Heidenreich, Hunter
%A Williams, Jake
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F heidenreich-williams-2019-latent
%X The Princeton WordNet is a powerful tool for studying language and developing natural language processing algorithms. With significant work developing it further, one line considers its extension through aligning its expert-annotated structure with other lexical resources. In contrast, this work explores a completely data-driven approach to network construction, forming a wordnet using the entirety of the open-source, noisy, user-annotated dictionary, Wiktionary. Comparing baselines to WordNet, we find compelling evidence that our network induction process constructs a network with useful semantic structure. With thousands of semantically-linked examples that demonstrate sense usage from basic lemmas to multiword expressions (MWEs), we believe this work motivates future research.
%R 10.18653/v1/D19-5523
%U https://aclanthology.org/D19-5523
%U https://doi.org/10.18653/v1/D19-5523
%P 170-180

Markdown (Informal)

[Latent semantic network induction in the context of linked example senses](https://aclanthology.org/D19-5523) (Heidenreich & Williams, WNUT 2019)

• Latent semantic network induction in the context of linked example senses (Heidenreich & Williams, WNUT 2019)

ACL

• Hunter Heidenreich and Jake Williams. 2019. Latent semantic network induction in the context of linked example senses. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 170–180, Hong Kong, China. Association for Computational Linguistics.