Rhetorical relations markers in Russian RST Treebank

Toldova, Svetlana; Pisarevskaya, Dina; Margarita, Ananyeva; Kobozeva, Maria; Nasedkin, Alexander; Nikiforova, Sofia; Shelepov, Alexey

doi:10.18653/v1/w17-3604

Cited by 12 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed work was performed as part of an on-going research project aimed at creation of discourse annotated corpus of popular science texts written in Russian. It includes 68 articles on linguistics and 11 texts taken from the open corpus of Ru-RSTreebank [11], [17], [18]. Popular science discourse is defined as a way of transmitting scientific knowledge or innovation projects by the author-scientist (or a journalist as an intermediary) for their understanding by a mass audience.…”

Section: Annotation Modelmentioning

confidence: 99%

Comparative Analysis of Rhetorical and Argumentative Structures in the Study of Popular Science Discourse

Kononenko¹,

Sidorova²,

Akhmadeeva³

2020

Computational Linguistics and Intellectual Technologies

View full text Add to dashboard Cite

The proposed work is performed as a part of an on-going research project aimed at creation of discourse annotated corpus of popular science texts written in Russian. Annotation is carried out within the framework of a multi-level model of discourse, which considers the text from the perspective of genre, rhetorical and argumentative organization. We conduct a comparative study of the rhetorical and argument annotations, discuss their similarities and differences on the segment and structural levels and show them on the examples of standard schemes of reasoning described in D. Walton’s theory of structured argumentation: “Argument from Expert Opinion”, “Argument from Example”, and “Argument from Cause to Effect”. Special attention is paid to discourse markers registered during annotation as key indicators of discourse structure. We report the results of the experiment with argument indicator patterns, based on the list of rhetorical markers, and aimed at the extraction of “from Expert Opinion” arguments.

show abstract

Section: Annotation Modelmentioning

confidence: 99%

Comparative Analysis of Rhetorical and Argumentative Structures in the Study of Popular Science Discourse

Kononenko¹,

Sidorova²,

Akhmadeeva³

2020

Computational Linguistics and Intellectual Technologies

View full text Add to dashboard Cite

show abstract

“…SDRT (Asher et al, 2003) was proposed as an extension to Discourse Representation Theory Corpus name Language Annotation style Tokens RSTBT (Iruskieta et al, 2013) Basque RST 28,658 CDTB (Zhou and Xue, 2015) Chinese PDTB 63,239 SCTB (Cao et al, 2018) Chinese RST 11,067 NLDT (Redeker et al, 2012) Dutch RST 21,355 PDTB (Prasad et al, 2008) English PDTB 1,100,990 GUM (Zeldes, 2017) English RST 82,691 RSTDT (Carlson et al, 2002) English RST 184,158 STAC (Asher et al, 2016) English SDRT 41,666 ANNODIS (Afantenos et al, 2012) French SDRT 25,050 PCC (Stede and Neumann, 2014) German RST 29,883 RRST (Toldova et al, 2017) Russian RST 243,896 RSTSTB (da Cunha et al, 2011) Spanish RST 50,565 SCTB (Cao et al, 2018) Spanish RST 12,699 CSTN (Cardoso et al, 2011) Brazilian Portuguese RST 51,041 (Kamp, 1981). By including propositions as variables to reason over and discourse relations to rule out certain antecedents or promote others, it accounts for relations in a text beyond the sentence level (where dynamic semantic approaches often fail).…”

Section: Sdrtmentioning

confidence: 99%

Multi-lingual and Cross-genre Discourse Unit Segmentation

Bourgonje¹,

Schafer²

2019

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

View full text Add to dashboard Cite

We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text into minimal discourse units that are involved in some coherence relation. We apply a RandomForest and an LSTM based approach for all data sets, and we improve over a simple baseline assuming simple sentence or clause-like segmentation. Performance however varies a lot depending on language, and more importantly genre, with f-scores ranging from 73.00 to 94.47.

show abstract

“…− rus.rst.rrt -Russian RST Treebank (Toldova et al, 2017). − spa.rst.sctb -RST Spanish-Chinese Treebank (Spanish) (Shuyuan et al, 2018).…”

Section: Shared Task Datamentioning

confidence: 99%

The

Zeldes¹,

Das²,

Maziero³

et al. 2019

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

View full text Add to dashboard Cite

In 2019, we organized the first iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task on Elementary Discourse Unit Segmentation and Connective Detection. In this paper we review the data included in the task, which cover 2.6 million manually annotated tokens from 15 datasets in 10 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plaintokenized versions of the data. * Discourse Relation Parsing and Treebanking (DIS-RPT): 7th Workshop on Rhetorical Structure Theory and Related Formalisms (https://sites.google.com/ view/disrpt2019) was held in conjunction with Annual Conference of the NAACL 2019 in Minneapolis, MN.

show abstract

Rhetorical relations markers in Russian RST Treebank

Cited by 12 publications

References 13 publications

Comparative Analysis of Rhetorical and Argumentative Structures in the Study of Popular Science Discourse

Comparative Analysis of Rhetorical and Argumentative Structures in the Study of Popular Science Discourse

Multi-lingual and Cross-genre Discourse Unit Segmentation

The

Contact Info

Product

Resources

About