eng.rst.gum

The Georgetown University Multilayer (GUM) corpus

To cite this corpus, please refer to the following article:

Zeldes, Amir (2017) "The GUM Corpus: Creating Multilayer Resources in the Classroom". Language Resources and Evaluation 51(3), 581–612.

Introduction

a corpus of English texts from twelve text types:

Interviews from Wikimedia News from Wikinews Travel guides from Wikivoyage How-to guides from wikiHow Academic writing from Creative Commons sources Biographies from Wikipedia Fiction from Creative Commons sources Online forum discussions from Reddit Face-to-face conversations from the Santa Barbara Corpus Political speeches OpenStax open access textbooks YouTube Creative Commons vlogs

The corpus is created as part of the course LING-367 (Computational Corpus Linguistics) at Georgetown University.

For more details see: https://gucorpling.org/gum

DISRPT 2021 shared task information

For the DISRPT 2021 shared task on elementary discourse unit segmentation, only 11 open text genres are included with plain text, while the remaining genre, containing Reddit forum discussions, must be reconstructed using the script in utils/process_underscores.py (see main repository README). The data follows the normal division into train, test and dev partitions used for other tasks (e.g. for the conll shared task on UD parsing).

POS tags and syntactic parses are manually annotated gold data.

Notes on segmentation

GUM RST guidelines follow the RST-DT segmentation guidelines for English, according to which most clauses, including adnominal and nested clauses are discourse units. This dataset contains discontinuous discourse units (split 'same-unit'). Note that the .conllu data contains some reconstructed ellipsis tokens with decimal IDs (e.g. 12.1); these do not appear in the other formats and are ignored in token index spans.