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Towards the Data-driven System for Rhetorical Parsing of Russian Texts.

.
Toldova S., Chistova E., Kobozeva M., Shelmanov A., Smirnov I., Pisarevskaya D.

Results of the first experimental evaluation of machine learning models trained on RuRSTreebank – first Russian corpus annotated within RST framework – are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, the ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 ± 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

Language: English
DOI
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Keywords: data-driven approachdiscourse parsingdiscourse treebanks

In book

Proceedings of DISRPT 2019 - The Workshop on Discourse Relation Parsing and Treebanking. NAACL HLT 2019
Association for Computational Linguistics, 2019.
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