OLAC Record

Title:TAC KBP Belief and Sentiment - Comprehensive Training and Evaluation Data 2016-2017
Access Rights:Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining
Bibliographic Citation:Tracey, Jennifer, Stephanie Strassel, and Michael Arrigo. TAC KBP Belief and Sentiment - Comprehensive Training and Evaluation Data 2016-2017 LDC2023T13. Web Download. Philadelphia: Linguistic Data Consortium, 2023
Contributor:Tracey, Jennifer
Strassel, Stephanie
Arrigo, Michael
Date (W3CDTF):2023
Date Issued (W3CDTF):2023-12-15
Description:*Introduction* TAC KBP Belief and Sentiment - Comprehensive Training and Evaluation Data 2016-2017 was developed by the Linguistic Data Consortium and contains training and evaluation data produced in support of the 2016 and 2017 TAC KBP Belief and Sentiment (BeSt) evaluation tracks. Text Analysis Conference (TAC) is a series of workshops organized by the National Institute of Standards and Technology (NIST). TAC was developed to encourage research in natural language processing and related applications by providing a large test collection, common evaluation procedures, and a forum for researchers to share their results. Through its various evaluations, the Knowledge Base Population (KBP) track of TAC encouraged the development of systems to match entities mentioned in natural texts with those appearing in a knowledge base, to extract novel information about entities from a document collection, and to add it to a new or existing knowledge base. The Belief and Sentiment track involved using a gold standard set of labeled entities, relations, and events in order to create a system to automatically label belief and sentiment about each possible target (entity, relation or event), as well as identifying the entity that holds the belief or sentiment. The goal of the BeSt track was to provide information about beliefs and sentiments held by entities toward other entities, as well as toward events and relations. More information about the TAC KBP Belief and Sentiment track and other TAC KBP evaluations can be found on the NIST TAC website. *Data* The data in this release includes all of the source documents, gold standard entity, relation, and event annotation, and belief and sentiment annotation. The source document collections cover Chinese, English, and Spanish newswire and discussion forums. The corresponding Knowledge Base (KB) for much of the data - a 2008 snapshot of Wikipedia - is contained in TAC KBP Reference Knowledge Base (LDC2014T16). All text data is encoded as 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. *Samples* Please view the following samples: * Source (XML) * BeSt Annotation (XML) * ERE Annotation (XML) *Updates* None at this time.
Extent:Corpus size: 74080 KB
ISLRN: 872-904-667-640-7
DOI: 10.35111/37dk-bb17
Mandarin Chinese
Language (ISO639):eng
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/LDC2023T13
Rights Holder:Portions © 2010 Agence France Presse, © 2013 New York Times, © 2009-2010 The Associated Press, © 2013 Xinhua News Agency, © 2019, 2020, 2023 Trustees of the University of Pennsylvania
Type (DCMI):Text
Type (OLAC):primary_text


Archive:  The LDC Corpus Catalog
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OaiIdentifier:  oai:www.ldc.upenn.edu:LDC2023T13
DateStamp:  2024-04-24
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Citation: Tracey, Jennifer; Strassel, Stephanie; Arrigo, Michael. 2023. Linguistic Data Consortium.
Terms: area_Asia area_Europe country_CN country_ES country_GB dcmi_Text iso639_cmn iso639_eng iso639_spa olac_primary_text

Up-to-date as of: Tue May 7 7:25:58 EDT 2024