Resource: DEFT Chinese Committed Belief Annotation

Reference DEFT Chinese Committed Belief Annotation
Date of Submission Feb. 19, 2019, 6:17 p.m.
Status accepted
ISLRN 233-896-127-699-9
Resource Type Primary Text
Media Type Text
Source
Language Mandarin Chinese
Format/MIME Type application/xml, text/plain
Size 6616 KB
Access Medium Web Download
Description

*Introduction*

DEFT Chinese Committed Belief Annotation was developed by the Linguistic Data Consortium (LDC) and consists of approximately 83,000 tokens of Chinese discussion forum text annotated for "committed belief," which marks the level of commitment displayed by the author to the truth of the propositions expressed in the text.

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 language resources.

*Data*

The source data is Chinese discussion forum web text collected by LDC. Annotations fall into one of four categories: committed belief, non-committed belief, reported belief and not applicable. Further information about the annotation methodology is contained in the documentation accompanying this release.

This publication contains 140 files (83,016 tokens). Annotation files are stored in XML format, and source documents are stored in plain text format. Both types of files are encoded in UTF-8.

*Acknowledgement*

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.

Version 1.0
Creator Stephanie Strassel , Jennifer Tracey , Michael Arrigo , Neil Kuster
Distributor Linguistic Data Consortium
Rights Holder Portions © 2019 Trustees of the University of Pennsylvania