TAC KBP Cold Start - Comprehensive Evaluation Data 2012-2017

Item Name: TAC KBP Cold Start - Comprehensive Evaluation Data 2012-2017
Author(s): Joe Ellis, Jeremy Getman, Stephanie Strassel
LDC Catalog No.: LDC2019T17
ISBN: 1-58563-908-7
ISLRN: 535-918-219-858-1
DOI: https://doi.org/10.35111/h7h3-zk92
Release Date: November 15, 2019
Member Year(s): 2019
DCMI Type(s): Text
Data Source(s): newswire, web collection, discussion forum
Project(s): TAC
Application(s): entity extraction, relation extraction, event detection, sentiment detection, knowledge base population
Language(s): Mandarin Chinese, English, Spanish
Language ID(s): cmn, eng, spa
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2019T17 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Ellis, Joe, Jeremy Getman, and Stephanie Strassel. TAC KBP Cold Start - Comprehensive Evaluation Data 2012-2017 LDC2019T17. Web Download. Philadelphia: Linguistic Data Consortium, 2019.
Related Works: View


TAC KBP Cold Start - Comprehensive Evaluation Data 2012-2017 was developed by the Linguistic Data Consortium (LDC) and contains Chinese, English and Spanish data produced in support of the TAC KBP Cold Start evaluation track conducted from 2012 to 2017. This includes source documents, queries, assessments, manual runs and final assessments.

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 encourages the development of systems that can match entities mentioned in natural texts with those appearing in a knowledge base and extract novel information about entities from a document collection and add it to a new or existing knowledge base.

In the Cold Start track, systems were evaluated on their ability to construct a new knowledge base (KB) from information provided in a text collection in combination with technologies developed in other TAC KBP tracks -- slot filling, information extraction, question answering and entity discovery & linking. Specifically, Cold Start systems were required to find all entities in the text, and the KB must have ideally included every person, organization, and geo-political entity as well as all of the targeted relations between them. To facilitate the evaluation of those KBs, LDC annotators created sets of queries, human-generated responses to the queries, and assessments of both human and system responses. More information about Cold Start and other TAC KBP evaluations can be found on the NIST TAC website.


The source data in this release is comprised of English and Spanish newswire and web text collected by LDC that was used in the 2012, 2014 and 2015 evaluations and in the 2016 pilot collection. The source collections for the 2016 and 2017 evaluations, which also includes Chinese data, are available in TAC KBP Evaluation Source Corpora 2016-2017 (LDC2019T12). The archived 2013 Cold Start source data collection is available from NIST upon request.

Queries are presented in XML, while all other documents are plain text. All text data is encoded as UTF-8. Tools provided by the evaluation track coordinators are also included.


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 authorised 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.


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