TAC KBP English Entity Linking - Comprehensive Training and Evaluation Data 2009-2013
| Item Name: | TAC KBP English Entity Linking - Comprehensive Training and Evaluation Data 2009-2013 |
| Author(s): | Joe Ellis, Jeremy Getman, Stephanie Strassel |
| LDC Catalog No.: | LDC2018T16 |
| ISBN: | 1-58563-849-8 |
| ISLRN: | 287-583-243-614-4 |
| DOI: | https://doi.org/10.35111/13g2-th80 |
| Release Date: | June 15, 2018 |
| Member Year(s): | 2018 |
| DCMI Type(s): | Text |
| Data Source(s): | newswire, discussion forum, web collection |
| Project(s): | TAC |
| Application(s): | entity extraction, information extraction, knowledge base population, knowledge representation |
| Language(s): | English |
| Language ID(s): | eng |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2018T16 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Ellis, Joe, Jeremy Getman, and Stephanie Strassel. TAC KBP English Entity Linking - Comprehensive Training and Evaluation Data 2009-2013 LDC2018T16. Web Download. Philadelphia: Linguistic Data Consortium, 2018. |
| Related Works: | View |
Introduction
TAC KBP English Entity Linking - Comprehensive Training and Evaluation Data 2009-2013 was developed by the Linguistic Data Consortium (LDC) and contains training and evaluation data produced in support of the TAC KBP English Entity Linking tasks in 2009, 2010, 2011, 2012, and 2013. It includes queries and gold standard entity type information, Knowledge Base links, and equivalence class clusters for NIL entities. Also included are the source documents for the queries, specifically, English newswire, discussion forum and web data. The corresponding knowledge base is available as TAC KBP Reference Knowledge Base (LDC2014T16). Also included in this package are the results of an Entity Linking IAA (Inter-Annotator Agreement) study conducted in 2010.
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.
English Entity Linking was first conducted as part of the 2009 TAC KBP evaluations. Its goal is to measure systems' ability to determine whether an entity, specified by a query, has a matching node in a reference knowledge base (KB) and, if so, to create a link between the two. If there is no matching node for a query entity in the KB, EL systems are required to cluster the mention together with others referencing the same entity. More information about the TAC KBP Entity Linking task and other TAC KBP evaluations can be found on the NIST TAC website.
Data
All source documents were originally released as XML but have been converted to text files for this release. This change was made primarily because the documents were used as text files during data development but also because some fail XML parsing.
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 this sample.
Updates
None at this time.