Resource: DEFT Chinese Light and Rich ERE Annotation
|Reference||DEFT Chinese Light and Rich ERE Annotation|
|Date of Submission||Aug. 24, 2020, 4:46 p.m.|
|Resource Type||Primary Text|
|Format/MIME Type||application/xml, text/plain|
|Access Medium||Web Download|
DEFT Chinese Light and Rich ERE Annotation was developed by the Linguistic Data Consortium (LDC) and consists of 157 Chinese discussion forum documents 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 entity mentions for the 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.
The source data in this release is Chinese discussion forum web text collected by LDC. All files (157) were annotated following Light ERE annotation guidelines; a subset (149) were also labeled with Rich ERE annotation.
Below is a data summary:
Source documents are in plain text format, annotation is in XML format, and both are UTF-8 encoded.
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.
|Creator||Stephanie Strassel , Justin Mott , Song Chen|
|Distributor||Linguistic Data Consortium|
|Rights Holder||Portions © 2020 Trustees of the University of Pennsylvania|