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        <title>A Comparison of Feature-Based and Neural Scansion of Poetry</title>
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        <namePart type="given">Manex</namePart>
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    <name type="personal">
        <namePart type="given">Iñaki</namePart>
        <namePart type="family">Alegria</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_003</identifier>
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%0 Conference Proceedings
%T A Comparison of Feature-Based and Neural Scansion of Poetry
%A Agirrezabal, Manex
%A Alegria, Iñaki
%A Hulden, Mans
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F agirrezabal-etal-2017-comparison
%X Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.
%R 10.26615/978-954-452-049-6_003
%U https://doi.org/10.26615/978-954-452-049-6_003
%P 18-23

Markdown (Informal)

[A Comparison of Feature-Based and Neural Scansion of Poetry](https://doi.org/10.26615/978-954-452-049-6_003) (Agirrezabal et al., RANLP 2017)

• A Comparison of Feature-Based and Neural Scansion of Poetry (Agirrezabal et al., RANLP 2017)

ACL

• Manex Agirrezabal, Iñaki Alegria, and Mans Hulden. 2017. A Comparison of Feature-Based and Neural Scansion of Poetry. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 18–23, Varna, Bulgaria. INCOMA Ltd..