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        <title>Infinite SCAN: An Infinite Model of Diachronic Semantic Change</title>
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        <namePart type="given">Seiichi</namePart>
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        <namePart type="given">Mamoru</namePart>
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    <name type="personal">
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    <abstract>In this study, we propose a Bayesian model that can jointly estimate the number of senses of words and their changes through time.The model combines a dynamic topic model on Gaussian Markov random fields with a logistic stick-breaking process that realizes Dirichlet process. In the experiments, we evaluated the proposed model in terms of interpretability, accuracy in estimating the number of senses, and tracking their changes using both artificial data and real data.We quantitatively verified that the model behaves as expected through evaluation using artificial data.Using the CCOHA corpus, we showed that our model outperforms the baseline model and investigated the semantic changes of several well-known target words.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.104</identifier>
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%0 Conference Proceedings
%T Infinite SCAN: An Infinite Model of Diachronic Semantic Change
%A Inoue, Seiichi
%A Komachi, Mamoru
%A Ogiso, Toshinobu
%A Takamura, Hiroya
%A Mochihashi, Daichi
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F inoue-etal-2022-infinite
%X In this study, we propose a Bayesian model that can jointly estimate the number of senses of words and their changes through time.The model combines a dynamic topic model on Gaussian Markov random fields with a logistic stick-breaking process that realizes Dirichlet process. In the experiments, we evaluated the proposed model in terms of interpretability, accuracy in estimating the number of senses, and tracking their changes using both artificial data and real data.We quantitatively verified that the model behaves as expected through evaluation using artificial data.Using the CCOHA corpus, we showed that our model outperforms the baseline model and investigated the semantic changes of several well-known target words.
%R 10.18653/v1/2022.emnlp-main.104
%U https://aclanthology.org/2022.emnlp-main.104
%U https://doi.org/10.18653/v1/2022.emnlp-main.104
%P 1605-1616

Markdown (Informal)

[Infinite SCAN: An Infinite Model of Diachronic Semantic Change](https://aclanthology.org/2022.emnlp-main.104) (Inoue et al., EMNLP 2022)

• Infinite SCAN: An Infinite Model of Diachronic Semantic Change (Inoue et al., EMNLP 2022)

ACL

• Seiichi Inoue, Mamoru Komachi, Toshinobu Ogiso, Hiroya Takamura, and Daichi Mochihashi. 2022. Infinite SCAN: An Infinite Model of Diachronic Semantic Change. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1605–1616, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.