OLAC Record oai:www.ldc.upenn.edu:LDC2026T04

Metadata
Title:		DEFT Chinese and English Light and Rich ERE Parallel Annotation
Access Rights:		Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining
Bibliographic Citation:		Chen, Song, et al. DEFT Chinese and English Light and Rich ERE Parallel Annotation LDC2026T04. Web Download. Philadelphia: Linguistic Data Consortium, 2026
Contributor:		Chen, Song
		Mott, Justin
		Bies, Ann
		Strassel, Stephanie
Date (W3CDTF):		2026
Date Issued (W3CDTF):		2026-04-15
Description:		*Introduction* DEFT Chinese and English Light and Rich ERE Parallel Annotation was developed by the Linguistic Data Consortium (LDC) and consists of 179 Chinese discussion forum documents and their English translations annotated for entities, relations, and events (ERE). DARPA's Deep Exploration and Filtering of Text (DEFT) program aimed to address remaining capability gaps in state-of-the-art natural language processing technologies related to inference, causal relationships and anomaly detection. LDC supported the DEFT program by collecting, creating and annotating a variety of data sources. Light ERE annotation labels mentions for a target set of entity, relation and event types between and among those entities, including coreference. Rich ERE annotation expands types and tagging in the entities, relations and events annotation tasks and replaces strict event coreference with a more loosely defined event hopper annotation. Further information about the annotation methodology is contained in the documentation accompanying this release. *Data* The source data and English translations in this release were drawn from BOLT Chinese Discussion Forum Parallel Training Data (LDC2017T05) which consists of discussion forum text originally collected and translated by LDC under the DARPA BOLT program. In this publication, 179 Chinese-English document pairs were annotated following Light ERE annotation guidelines; a subset of 171 Chinese-English document pairs were also labeled with Rich ERE annotation. Below is a data summary: Language ERE Files Characters Words Entities (mentions) Fillers Relations Event Hoppers (mentions) Chinese Light 179 135,075 90,050 5,338 (13,113) N/A 1,707 415 (508) English Light 179 N/A 107,493 3,789 (12,090) N/A 1,289 319 (382) Chinese Rich 171 127,458 84,972 5,974 (14,102) 607 1,946 1,138 (1,491) English Rich 171 N/A 101,191 5,873 (16,055) 906 2,092 2,285 (2,933) Source documents are presented in plain text format. Annotation files are presented in XML format. All files are UTF-8 Encoded. *Samples* Please view the following samples: * Chinese Source (TXT) * Chinese Light ERE (XML) * Chinese Rich ERE (XML) * English Source (TXT) * English Light ERE (XML) * English Rich ERE (XML) *Updates* No updates at this time. *Sponsorship* This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government.
Extent:		Corpus size: 30720 KB
Identifier:		LDC2026T04
		https://catalog.ldc.upenn.edu/LDC2026T04
		ISLRN: 412-299-386-426-5
		DOI: 10.35111/whmk-xz32
Language:		English
		Mandarin Chinese
Language (ISO639):		eng
		cmn
License:		LDC User Agreement for Non-Members: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf
Medium:		Distribution: Web Download
Publisher:		Linguistic Data Consortium
Publisher (URI):		https://www.ldc.upenn.edu
Relation (URI):		https://catalog.ldc.upenn.edu/docs/LDC2026T04
Rights Holder:		Portions © 2014, 2015, 2017, 2026 Trustees of the University of Pennsylvania
Type (DCMI):		Text
Type (OLAC):		primary_text
OLAC Info
Archive:		The LDC Corpus Catalog
Description:		http://www.language-archives.org/archive/www.ldc.upenn.edu
GetRecord:		OAI-PMH request for OLAC format
GetRecord:		Pre-generated XML file
OAI Info
OaiIdentifier:		oai:www.ldc.upenn.edu:LDC2026T04
DateStamp:		2026-04-16
GetRecord:		OAI-PMH request for simple DC format
Search Info
Citation:		Chen, Song; Mott, Justin; Bies, Ann; Strassel, Stephanie. 2026. Linguistic Data Consortium.
Terms:		area_Asia area_Europe country_CN country_GB dcmi_Text iso639_cmn iso639_eng olac_primary_text