RST Discourse Treebank
| Item Name: | RST Discourse Treebank |
| Author(s): | Lynn Carlson, Daniel Marcu, Mary Ellen Okurowski |
| LDC Catalog No.: | LDC2002T07 |
| ISBN: | 1-58563-223-6 |
| ISLRN: | 299-735-991-930-2 |
| DOI: | https://doi.org/10.35111/4w31-m996 |
| Release Date: | February 21, 2002 |
| Member Year(s): | 2002 |
| DCMI Type(s): | Text |
| Data Source(s): | newswire |
| Application(s): | message understanding, discourse analysis |
| Language(s): | English |
| Language ID(s): | eng |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2002T07 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Carlson, Lynn, Daniel Marcu, and Mary Ellen Okurowski. RST Discourse Treebank LDC2002T07. Web Download. Philadelphia: Linguistic Data Consortium, 2002. |
| Related Works: | View |
Introduction
Rhetorical Structure Theory (RST) Discourse Treebank was developed by researchers at the Information Sciences Institute (University of Southern California), the US Department of Defense and the Linguistic Data Consortium (LDC). It consists of 385 Wall Street Journal articles from the Penn Treebank annotated with discourse structure in the RST framework along with human-generated extracts and abstracts associated with the source documents.
In the RST framework (Mann and Thompson, 1988), a text's discourse structure can be represented as a tree in four aspects: (1) the leaves correspond to text fragments called elementary discourse units (the mininal discourse units); (2) the internal nodes of the tree correspond to contiguous text spans; (3) each node is characterized by its nuclearity, or essential unit of information; and (4) each node is also characterized by a rhetorical relation between two or more non-overlapping, adjacent text spans.
Data
The data in this release is divided into a training set (347 documents) and a test set (38 documents). All annotations were produced using a discourse annotation tool that can be downloaded from http://www.isi.edu/~marcu/discourse.
Human-generated material in the corpus includes (1) long and short abstracts for 30 documents that were intended to convey the essential information and the main topic of the article, respectively; and (2) long, short and informative extracts for 180 documents, some of which were created from scratch and some of which were derived from the humanly-producted abstracts indicated above.
Samples
Please view this sample.
Updates
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