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        <title>Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks</title>
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    <name type="personal">
        <namePart type="given">Oliver</namePart>
        <namePart type="family">Hellwig</namePart>
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    <name type="personal">
        <namePart type="given">Sebastian</namePart>
        <namePart type="family">Nehrdich</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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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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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi). Tokenization of Sanskrit depends on local phonetic and distant semantic features that are incorporated using convolutional and recurrent elements. Contrary to most previous systems, our models do not require feature engineering or extern linguistic resources, but operate solely on parallel versions of raw and segmented text. The models discussed in this paper clearly improve over previous approaches to Sanskrit word segmentation. As they are language agnostic, we will demonstrate that they also outperform the state of the art for the related task of German compound splitting.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1295</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks
%A Hellwig, Oliver
%A Nehrdich, Sebastian
%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 hellwig-nehrdich-2018-sanskrit
%X The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi). Tokenization of Sanskrit depends on local phonetic and distant semantic features that are incorporated using convolutional and recurrent elements. Contrary to most previous systems, our models do not require feature engineering or extern linguistic resources, but operate solely on parallel versions of raw and segmented text. The models discussed in this paper clearly improve over previous approaches to Sanskrit word segmentation. As they are language agnostic, we will demonstrate that they also outperform the state of the art for the related task of German compound splitting.
%R 10.18653/v1/D18-1295
%U https://aclanthology.org/D18-1295
%U https://doi.org/10.18653/v1/D18-1295
%P 2754-2763

Markdown (Informal)

[Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks](https://aclanthology.org/D18-1295) (Hellwig & Nehrdich, EMNLP 2018)

• Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks (Hellwig & Nehrdich, EMNLP 2018)

ACL

• Oliver Hellwig and Sebastian Nehrdich. 2018. Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2754–2763, Brussels, Belgium. Association for Computational Linguistics.