Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory

By Lynn Carlson, Daniel Marcu, Mary Ellen Okurovsky. SIGDIAL Workshop, 2001

895 citations recorded by the source corpus.

We describe our experience in developing a discourse-annotated corpus for community-wide use. Working in the framework of Rhetorical Structure Theory, we were able to create a large annotated resource with very high consistency, using a well-defined methodology and protocol. This resource is made publicly available through the Linguistic Data Consortium to enable researchers to develop empirically grounded, discourse-specific applications.

Subfields: Artificial Intelligence, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/07a78850c0c2ff11acf21fccca40bfcb79da282b

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Citations in the corpus

Identifying Argumentative Discourse Structures in Persuasive Essays (2014, background): “Indeed, Marcu and Echihabi (2002) found that only 26% of the evidence relations in the RST Discourse Treebank (Carlson et al., 2001) include discourse markers.”
Underreporting of errors in NLG output, and what to do about it (2021, background): “Taking into consideration the costly process of selecting domain expert annotators and the importance of quality control, nondomain experts might be also considered, ensuring their qualification through (intensive) training (Artstein and Poesio, 2005; Carlson et al., 2001).”
When Are Tree Structures Necessary for Deep Learning of Representations? (2015, methodology): “Our ﬁnal task, discourse parsing based on the RST-DT corpus (Carlson et al., 2003), is to build a discourse tree for a document, based on assigning Rhetorical Structure Theory (RST) relations between elementary discourse units (EDUs).”
Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation (2016, background): “…which do annotate causal, temporal, event-structural or coreference relations within limited scopes, such as within the same clause or across adjacent sentences (Banarescu et al., 2013; Carlson et al., 2003; Prasad et al., 2008; Riaz and Girju, 2013; Fillmore and Baker, 2000; Palmer et al., 2005).”
Parsing Argumentation Structures in Persuasive Essays (2016, methodology): “They selected a subset of 12 relations from the RST Discourse Treebank (Carlson, Marcu, and Okurowski 2001) and argue that only a subset of RST relations is relevant for identifying justiﬁcations.”
The GUM corpus: creating multilayer resources in the classroom (2016, methodology): “Examples include coreference analysis or named entities (e.g. for WSJ in OntoNotes, Hovy et al. 2006, which was extended to include Mandarin Chinese and Modern Standard Arabic), phonetic and further disfluency annotation or prosody and ToBI breaks (SWBD, Calhoun et al. 2010), as well as discourse fu”
Automatic sense prediction for implicit discourse relations in text (2009, methodology): “They use the RST corpus (Carlson et al., 2001), which contains 385 Wall Street Journal articles annotated following the Rhetorical Structure Theory (Mann and Thompson, 1988).”
An Unsupervised Approach to Recognizing Discourse Relations (2002, methodology): “(In the RST corpus built by Carlson et al. (2001), 89 out of the 106 occurrences of “but” that occur at the beginning of a sentence signal a CONTRAST relation that holds between the sentence that contains the word “but” and the sentence that precedes it.)”
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints (2016, methodology): “10 We also compare to our implementation of the Tree Knapsack method of Yoshida et al. (2014), which matches their results very closely on the RST Discourse Treebank when discourse trees are controlled for.”
Revisiting Readability: A Unified Framework for Predicting Text Quality (2008, background): “In the past, subsections of the Penn Treebank (Marcus et al., 1994) have been annotated for discourse relations (Carlson et al., 2001; Wolf and Gibson, 2005).”
From Argument Diagrams to Argumentation Mining in Texts: A Survey (2013, methodology): “These authors base their supervised learning on the RST Discourse Treebank (Carlson et al., 2003) and employ two different classifiers:”
Learning Subjective Language (2004, positive): “In addition, it would be illuminating to apply our system to data annotated with discourse trees (Carlson, Marcu, and Okurowski 2001).”
FactBank: a corpus annotated with event factuality (2009, methodology): “1998), RST Corpus ( Carlson et al. 2003 ), Penn Discourse TreeBank (Miltsakaki et al. 2004), GraphBank (Wolf and Gibson 2005), TimeBank (Pustejovsky et al. 2006), MPQA Opinion Corpus (Wiebe et al. 2005).”
Cross-lingual and cross-domain discourse segmentation of entire documents (2017, methodology): ““[when rates are rising] [and shift out at times]” – that should be split (Carlson et al., 2001), a distinction hard to make without syntactic trees.”
The Penn Discourse Treebank (2004, background): “At the discourse-level are efforts to produce corpora annotated with RST rhetorical relations (Carlson et al., 2003), coreference (Mitkov et al.”