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        <title>Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction</title>
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        <namePart type="given">Karl</namePart>
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            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
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        <name type="personal">
            <namePart type="given">Christy</namePart>
            <namePart type="family">Doran</namePart>
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    <abstract>We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent (SGD): a novel generalization of the classical Brown clustering objective and a recently proposed variational lower bound. While both objectives are subject to noise in gradient updates, we show through analysis and experiments that the variational lower bound is robust whereas the generalized Brown objective is vulnerable. We obtain strong performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1113</identifier>
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%0 Conference Proceedings
%T Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction
%A Stratos, Karl
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F stratos-2019-mutual
%X We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent (SGD): a novel generalization of the classical Brown clustering objective and a recently proposed variational lower bound. While both objectives are subject to noise in gradient updates, we show through analysis and experiments that the variational lower bound is robust whereas the generalized Brown objective is vulnerable. We obtain strong performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context.
%R 10.18653/v1/N19-1113
%U https://aclanthology.org/N19-1113
%U https://doi.org/10.18653/v1/N19-1113
%P 1095-1104

Markdown (Informal)

[Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction](https://aclanthology.org/N19-1113) (Stratos, NAACL 2019)

• Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction (Stratos, NAACL 2019)

ACL

• Karl Stratos. 2019. Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1095–1104, Minneapolis, Minnesota. Association for Computational Linguistics.