Towards the Data-driven System for Rhetorical Parsing of

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

doi:10.18653/v1/w19-2711

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Both the comparison table and the result of the evaluation can be obtained automatically. Shelmanov, Pisarevskaya, Chistova, Toldova, Kobozeva and Smirnov (Shelmanov et al, 2019) present results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank (the first Russian corpus annotated within the RST framework). Various lexical, quantitative, morphological, and semantic features were used.…”

Section: Workhop Papersmentioning

confidence: 99%

Introduction to Discourse Relation Parsing and Treebanking (

Zeldes¹,

Das²,

Maziero³

et al. 2019

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop's aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.

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Section: Workhop Papersmentioning

confidence: 99%

Introduction to Discourse Relation Parsing and Treebanking (

Zeldes¹,

Das²,

Maziero³

et al. 2019

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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COLIEE 2020: Legal Information Retrieval and Entailment with Legal Embeddings and Boosting

Alberts¹,

Akin²,

Lucas³

et al. 2021

New Frontiers in Artificial Intelligence

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Algorithm of Constituency Tree from Dependency Tree Construction for a Russian-Language Sentence

Poletaev,

Paramonov,

Boychuk

2023

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Automatic syntactic analysis of a sentence is an important computational linguistics task. At present, there are no syntactic structure parsers for Russian that are publicly available and suitable for practical applications. Ground-up creation of such parsers requires building of a treebank annotated according to a given formal grammar, which is quite a cumbersome task. However, since there are several syntactic dependency parsers for Russian, it seems reasonable to employ dependency parsing results for syntactic structure analysis. The article introduces an algorithm that allows to construct the constituency tree of a Russian sentence by a syntactic dependency tree. The formal grammar used by the algorithm is based on the D.E. Rosenthal’s classic reference. The algorithm was evaluated on 300 Russian-language sentences. 200 of them were selected from the aforementioned reference, and 100 from OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. During the evaluation, the sentences were passed to syntactic dependency parsers from Stanza, SpaCy, and Natasha packages, then the resulted dependency trees were processed by the proposed algorithm. The obtained constituency trees were compared with the trees manually annotated by experts in linguistics. The best performance was achieved using the Stanza parser: the constituency parsing F1–score was 0.85, and the sentence parts tagging accuracy was 0.93, that would be sufficient for many practical applications, such as event extraction, information retrieval and sentiment analysis.

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Towards the Data-driven System for Rhetorical Parsing of

Cited by 3 publications

References 14 publications

Introduction to Discourse Relation Parsing and Treebanking (

Introduction to Discourse Relation Parsing and Treebanking (

COLIEE 2020: Legal Information Retrieval and Entailment with Legal Embeddings and Boosting

Algorithm of Constituency Tree from Dependency Tree Construction for a Russian-Language Sentence

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