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

Artem Shelmanov, Dina Pisarevskaya, Elena Chistova, Svetlana Toldova, Maria Kobozeva, Ivan Smirnov


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 ± 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.
Anthology ID:
W19-2711
Volume:
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019
Month:
June
Year:
2019
Address:
Minneapolis, MN
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
82–87
Language:
URL:
https://aclanthology.org/W19-2711
DOI:
10.18653/v1/W19-2711
Bibkey:
Cite (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.
Cite (Informal):
Towards the Data-driven System for Rhetorical Parsing of Russian Texts (Shelmanov et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2711.pdf
Poster:
 W19-2711.Poster.pdf