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        <title>Multi-lingual and Cross-genre Discourse Unit Segmentation</title>
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            <title>Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019</title>
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        <name type="personal">
            <namePart type="given">Amir</namePart>
            <namePart type="family">Zeldes</namePart>
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        <name type="personal">
            <namePart type="given">Debopam</namePart>
            <namePart type="family">Das</namePart>
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        <name type="personal">
            <namePart type="given">Erick</namePart>
            <namePart type="given">Maziero</namePart>
            <namePart type="family">Galani</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Juliano</namePart>
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            <namePart type="family">Antonio</namePart>
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            <namePart type="given">Mikel</namePart>
            <namePart type="family">Iruskieta</namePart>
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    <abstract>We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text into minimal discourse units that are involved in s coherence relation. We apply a RandomForest and an LSTM based approach for all data sets, and we improve over a simple baseline assuming simple sentence or clause-like segmentation. Performance however varies a lot depending on language, and more importantly genre, with f-scores ranging from 73.00 to 94.47.</abstract>
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%0 Conference Proceedings
%T Multi-lingual and Cross-genre Discourse Unit Segmentation
%A Bourgonje, Peter
%A Schäfer, Robin
%Y Zeldes, Amir
%Y Das, Debopam
%Y Galani, Erick Maziero
%Y Antonio, Juliano Desiderato
%Y Iruskieta, Mikel
%S Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, MN
%F bourgonje-schafer-2019-multi
%X We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text into minimal discourse units that are involved in s coherence relation. We apply a RandomForest and an LSTM based approach for all data sets, and we improve over a simple baseline assuming simple sentence or clause-like segmentation. Performance however varies a lot depending on language, and more importantly genre, with f-scores ranging from 73.00 to 94.47.
%R 10.18653/v1/W19-2714
%U https://aclanthology.org/W19-2714
%U https://doi.org/10.18653/v1/W19-2714
%P 105-114

Markdown (Informal)

[Multi-lingual and Cross-genre Discourse Unit Segmentation](https://aclanthology.org/W19-2714) (Bourgonje & Schäfer, NAACL 2019)

• Multi-lingual and Cross-genre Discourse Unit Segmentation (Bourgonje & Schäfer, NAACL 2019)

ACL

• Peter Bourgonje and Robin Schäfer. 2019. Multi-lingual and Cross-genre Discourse Unit Segmentation. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 105–114, Minneapolis, MN. Association for Computational Linguistics.