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        <title>Towards the Data-driven System for Rhetorical Parsing of Russian Texts</title>
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        <namePart type="given">Artem</namePart>
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    <name type="personal">
        <namePart type="given">Dina</namePart>
        <namePart type="family">Pisarevskaya</namePart>
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    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Chistova</namePart>
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    <name type="personal">
        <namePart type="given">Svetlana</namePart>
        <namePart type="family">Toldova</namePart>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Kobozeva</namePart>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
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            <title>Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019</title>
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    <abstract>Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank – first Russian corpus annotated within RST framework – are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 $\pm$ 0.38). We discover that most of the important features for rhetorical relation classification are related to discourse connectives derived from the connectives lexicon for Russian and from other sources.</abstract>
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    <identifier type="doi">10.18653/v1/W19-2711</identifier>
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%0 Conference Proceedings
%T Towards the Data-driven System for Rhetorical Parsing of Russian Texts
%A Shelmanov, Artem
%A Pisarevskaya, Dina
%A Chistova, Elena
%A Toldova, Svetlana
%A Kobozeva, Maria
%A Smirnov, Ivan
%S Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, MN
%F shelmanov-etal-2019-towards
%X Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank – first Russian corpus annotated within RST framework – are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 $\pm$ 0.38). We discover that most of the important features for rhetorical relation classification are related to discourse connectives derived from the connectives lexicon for Russian and from other sources.
%R 10.18653/v1/W19-2711
%U https://aclanthology.org/W19-2711
%U https://doi.org/10.18653/v1/W19-2711
%P 82-87

Markdown (Informal)

[Towards the Data-driven System for Rhetorical Parsing of Russian Texts](https://aclanthology.org/W19-2711) (Shelmanov et al., 2019)

• Towards the Data-driven System for Rhetorical Parsing of Russian Texts (Shelmanov et al., 2019)

ACL

• Artem Shelmanov, Dina Pisarevskaya, Elena Chistova, Svetlana Toldova, Maria Kobozeva, and Ivan Smirnov. 2019. Towards the Data-driven System for Rhetorical Parsing of Russian Texts. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 82–87, Minneapolis, MN. Association for Computational Linguistics.