TAC KBP Entity Discovery and Linking - Comprehensive Training and Evaluation Data 2014-2015

Item Name: TAC KBP Entity Discovery and Linking - Comprehensive Training and Evaluation Data 2014-2015
Author(s): Joe Ellis, Jeremy Getman, Stephanie Strassel
LDC Catalog No.: LDC2019T02
ISBN: 1-58563-872-2
ISLRN: 332-250-848-144-8
DOI: https://doi.org/10.35111/kp6d-qf49
Release Date: January 15, 2019
Member Year(s): 2019
DCMI Type(s): Text
Data Source(s): discussion forum, broadcast news, web collection
Project(s): TAC
Application(s): entity extraction, information extraction, knowledge base population, knowledge representation
Language(s): English, Mandarin Chinese, Spanish
Language ID(s): eng, cmn, spa
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2019T02 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Ellis, Joe, Jeremy Getman, and Stephanie Strassel. TAC KBP Entity Discovery and Linking - Comprehensive Training and Evaluation Data 2014-2015 LDC2019T02. Web Download. Philadelphia: Linguistic Data Consortium, 2019.
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TAC KBP Entity Discovery and Linking - Comprehensive Training and Evaluation Data 2014-2015 was developed by the Linguistic Data Consortium (LDC) and contains training and evaluation data produced in support of the TAC KBP Entity Discovery and Linking (EDL) tasks in 2014 and 2015. It includes queries, knowledge base links, equivalence class clusters for NIL entities, and entity type information for each of the queries. Also included in this data set are all necessary source documents as well as BaseKB - the second reference KB that was adopted for use by EDL in 2015. The first EDL reference KB to which 2014 EDL data are linked is available separately as TAC KBP Reference Knowledge Base (LDC2014T16).

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.

The goal of the Entity Discovery and Linking (EDL) track is to conduct end-to-end entity extraction, linking and clustering. For producing gold standard data, given a document collection, annotators (1) extract (identify and classify) entity mentions (queries), link them to nodes in a reference Knowledge Base (KB) and (2) perform cross-document co-reference on within-document entity clusters that cannot be linked to the KB. More information about the TAC KBP EDL task and other TAC KBP evaluations can be found on the NIST TAC website.


Source data consists of Chinese, English and Spanish newswire and web text collected by LDC. The EDL 2014 task involved English data only. Chinese and Spanish data were added in the 2015 task. A summary of the data by year, task, source documents and mentions is below:

Year Task Source Documents Mentions
2014 eval 138 5598
2014 training 160 6349
2015 pilot 15 686
2015 training 444 30,834
2015 eval 500 32,459


Source documents are presented as UTF-8 encoded XML documents.


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.


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