TAC KBP Entity Discovery and Linking - Comprehensive Evaluation Data 2016-2017
| Item Name: | TAC KBP Entity Discovery and Linking - Comprehensive Evaluation Data 2016-2017 |
| Author(s): | Joe Ellis, Jeremy Getman, Stephanie Strassel |
| LDC Catalog No.: | LDC2019T19 |
| ISBN: | 1-58563-910-9 |
| ISLRN: | 733-869-870-645-2 |
| DOI: | https://doi.org/10.35111/qex0-ny26 |
| Release Date: | December 05, 2019 |
| Member Year(s): | 2019 |
| DCMI Type(s): | Text |
| Data Source(s): | discussion forum, newswire |
| Project(s): | TAC |
| Application(s): | entity extraction, information extraction, knowledge representation |
| Language(s): | Mandarin Chinese, English, Spanish |
| Language ID(s): | cmn, eng, spa |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2019T19 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Ellis, Joe, Jeremy Getman, and Stephanie Strassel. TAC KBP Entity Discovery and Linking - Comprehensive Evaluation Data 2016-2017 LDC2019T19. Web Download. Philadelphia: Linguistic Data Consortium, 2019. |
| Related Works: | View |
Introduction
TAC KBP Entity Discovery and Linking - Comprehensive Evaluation Data 2016-2017 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 2016 and 2017. It includes queries, knowledge base (KB) links, equivalence class clusters for NIL entities, and entity type information for each of the queries. The EDL reference KB, to which EDL data are linked, is available separately in TAC KBP Entity Discovery and Linking - Comprehensive Training and Evaluation Data 2014-2015 (LDC2019T02). Source documents referenced by the files in this package are available separately in TAC KBP Evaluation Source Corpora 2016-2017 (LDC2019T12).
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.
Data
Source data for the annotations in this corpus was Chinese, English and Spanish newswire and discussion forum text collected by LDC. The annotation files are presented as UTF-8 encoded tab delimited files.
A summary of the data by year, task, and mentions is below:
| Year | Task | Mentions |
| 2016 | eval | 24,373 |
| 2017 | eval | 25,040 |
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.