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        <title>Dynamic Generative model for Diachronic Sense Emergence Detection</title>
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        <namePart type="given">Martin</namePart>
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        <namePart type="given">Arun</namePart>
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        <dateIssued>2016-12</dateIssued>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>As time passes words can acquire meanings they did not previously have, such as the ‘twitter post’ usage of ‘tweet’. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the ‘pseudo-word’ technique (Schutze, 1992) to give a novel further evaluation via ‘pseudo-neologisms’. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.</abstract>
    <identifier type="citekey">emms-jayapal-2016-dynamic</identifier>
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        <url>https://aclanthology.org/C16-1129</url>
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%0 Conference Proceedings
%T Dynamic Generative model for Diachronic Sense Emergence Detection
%A Emms, Martin
%A Jayapal, Arun Kumar
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F emms-jayapal-2016-dynamic
%X As time passes words can acquire meanings they did not previously have, such as the ‘twitter post’ usage of ‘tweet’. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the ‘pseudo-word’ technique (Schutze, 1992) to give a novel further evaluation via ‘pseudo-neologisms’. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.
%U https://aclanthology.org/C16-1129
%P 1362-1373

Markdown (Informal)

[Dynamic Generative model for Diachronic Sense Emergence Detection](https://aclanthology.org/C16-1129) (Emms & Jayapal, COLING 2016)

• Dynamic Generative model for Diachronic Sense Emergence Detection (Emms & Jayapal, COLING 2016)

ACL

• Martin Emms and Arun Kumar Jayapal. 2016. Dynamic Generative model for Diachronic Sense Emergence Detection. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1362–1373, Osaka, Japan. The COLING 2016 Organizing Committee.