@inproceedings{shelmanov-etal-2019-towards,
title = "Towards the Data-driven System for Rhetorical Parsing of {R}ussian Texts",
author = "Chistova, Elena and
Kobozeva, Maria and
Pisarevskaya, Dina and
Shelmanov, Artem and
Smirnov, Ivan and
Toldova, Svetlana",
editor = "Zeldes, Amir and
Das, Debopam and
Galani, Erick Maziero and
Antonio, Juliano Desiderato and
Iruskieta, Mikel",
booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
month = jun,
year = "2019",
address = "Minneapolis, MN",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2711",
doi = "10.18653/v1/W19-2711",
pages = "82--87",
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 {\mbox{$\pm$}} 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.",
}
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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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%0 Conference Proceedings
%T Towards the Data-driven System for Rhetorical Parsing of Russian Texts
%A Chistova, Elena
%A Kobozeva, Maria
%A Pisarevskaya, Dina
%A Shelmanov, Artem
%A Smirnov, Ivan
%A Toldova, Svetlana
%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 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) (Chistova et al., NAACL 2019)
ACL