TAC KBP English Event Argument - Training and Evaluation Data 2014-2015

Item Name: TAC KBP English Event Argument - Training and Evaluation Data 2014-2015
Author(s): Joe Ellis, Jeremy Getman, Stephanie Strassel
LDC Catalog No.: LDC2020T03
ISBN: 1-58563-918-4
ISLRN: 505-557-960-269-8
DOI: https://doi.org/10.35111/th16-k019
Release Date: February 17, 2020
Member Year(s): 2020
DCMI Type(s): Text
Data Source(s): newswire, discussion forum
Project(s): TAC
Application(s): event detection, knowledge base population
Language(s): English
Language ID(s): eng
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2020T03 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Ellis, Joe, Jeremy Getman, and Stephanie Strassel. TAC KBP English Event Argument - Training and Evaluation Data 2014-2015 LDC2020T03. Web Download. Philadelphia: Linguistic Data Consortium, 2020.
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TAC KBP English Event Argument - 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 2014 TAC KBP English Event Argument Extraction Pilot and Evaluation tasks and the 2015 English Event Argument Extraction and Linking Training and Evaluation tasks.

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 Event Argument Extraction and Linking task required systems to extract event arguments (entities or attributes playing a role in an event) from unstructured text, indicate the role they play in an event, and link the arguments appearing in the same event to each other. Since the extracted information must be suitable as input to a knowledge base, systems constructed tuples indicating the event type, the role played by the entity in the event, and the most canonical mention of the entity from the source document. The event types and roles were drawn from an externally-specified ontology of 31 event types, which included financial transactions, communication events, and attacks. For more information about Event Argument Extraction and Linking, refer to the track home page on the NIST TAC website.


Source data for the annotations in this corpus was English newswire and discussion forum text collected by LDC. Source and annotation files are presented as UTF-8 encoded tab delimited or plain text files.

A summary of the data is below:

Year Set Source Docs Manual Responses Assessments Event Hoppers
2014 pilot 60 0 32,054 n/a
2014 evaluation 528 5,947 57,599 n/a
2015 training 55* 0 0 599
2015 evaluation 500 5,207 45,391 1,608

*NOTE: the 2015 training source documents are a subset of the 2014 evaluation source corpus


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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