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        <title>Analyzing Correlated Evolution of Multiple Features Using Latent Representations</title>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Statistical phylogenetic models have allowed the quantitative analysis of the evolution of a single categorical feature and a pair of binary features, but correlated evolution involving multiple discrete features is yet to be explored. Here we propose latent representation-based analysis in which (1) a sequence of discrete surface features is projected to a sequence of independent binary variables and (2) phylogenetic inference is performed on the latent space. In the experiments, we analyze the features of linguistic typology, with a special focus on the order of subject, object and verb. Our analysis suggests that languages sharing the same word order are not necessarily a coherent group but exhibit varying degrees of diachronic stability depending on other features.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1468</identifier>
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%0 Conference Proceedings
%T Analyzing Correlated Evolution of Multiple Features Using Latent Representations
%A Murawaki, Yugo
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F murawaki-2018-analyzing
%X Statistical phylogenetic models have allowed the quantitative analysis of the evolution of a single categorical feature and a pair of binary features, but correlated evolution involving multiple discrete features is yet to be explored. Here we propose latent representation-based analysis in which (1) a sequence of discrete surface features is projected to a sequence of independent binary variables and (2) phylogenetic inference is performed on the latent space. In the experiments, we analyze the features of linguistic typology, with a special focus on the order of subject, object and verb. Our analysis suggests that languages sharing the same word order are not necessarily a coherent group but exhibit varying degrees of diachronic stability depending on other features.
%R 10.18653/v1/D18-1468
%U https://aclanthology.org/D18-1468
%U https://doi.org/10.18653/v1/D18-1468
%P 4371-4382

Markdown (Informal)

[Analyzing Correlated Evolution of Multiple Features Using Latent Representations](https://aclanthology.org/D18-1468) (Murawaki, EMNLP 2018)

• Analyzing Correlated Evolution of Multiple Features Using Latent Representations (Murawaki, EMNLP 2018)

ACL

• Yugo Murawaki. 2018. Analyzing Correlated Evolution of Multiple Features Using Latent Representations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4371–4382, Brussels, Belgium. Association for Computational Linguistics.