Datasets for Generic Relation Extraction (reACE)
| Item Name: | Datasets for Generic Relation Extraction (reACE) |
| Author(s): | Benjamin Hachey, Claire Grover, Richard Tobin |
| LDC Catalog No.: | LDC2011T08 |
| ISBN: | 1-58563-582-0 |
| ISLRN: | 494-554-511-556-5 |
| DOI: | https://doi.org/10.35111/6mma-3a80 |
| Release Date: | June 17, 2011 |
| Member Year(s): | 2011 |
| DCMI Type(s): | Text |
| Data Source(s): | newswire, broadcast news |
| Project(s): | ACE |
| Application(s): | relation extraction |
| Language(s): | English |
| Language ID(s): | eng |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2011T08 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Hachey, Benjamin, Claire Grover, and Richard Tobin. Datasets for Generic Relation Extraction (reACE) LDC2011T08. Web Download. Philadelphia: Linguistic Data Consortium, 2011. |
| Related Works: | View |
Introduction
Datasets for Generic Relation Extraction (reACE) was developed at The University of Edinburgh, Edinburgh, Scotland. It consists of English broadcast news and newswire data originally annotated for the ACE (Automatic Content Extraction) program to which the Edinburgh Regularized ACE (reACE) mark-up has been applied.
The Edinburgh relation extraction (RE) task aims to identify useful information in text (e.g., PersonW works for OrganisationX, GeneY encodes ProteinZ) and to recode it in a format such as a relational database or RDF triple store (a database for the storage and retreival of Resource Description Framework (RDF) metadata) that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluation of automatic systems for RE in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup formats and different notions of what constitutes a relation.
reACE solves this problem by converting data to a common document type using token standoff and including detailed linguistic markup while maintaining all information in the original annotation. The subsequent reannotation process normalises the two data sets so that they comply with a notion of relation that is intuitive, simple and informed by the semantic web.
The data in this corpus consists of newswire and broadcast news material from ACE 2004 Multilingual Training Corpus LDC 2005T09 and ACE 2005 Multilingual Training Corpus LDC2006T06. This material has been standardised for evaluation of multi-type RE across domains.
Complete documentation for this corpus is available at the publication providers web site Datasets for Generic Relation Extraction.
Data
Annotation includes (1) a refactored version of the original data to a common XML document type (2) linguistic information from LT-TTT (a system for tokenizing text and adding markup) and MINIPAR (an English parser) and (3) a normalised version of the original RE markup that complies with a shared notion of what constitutes a relation across domains.
The data sources represented in the corpus were collected by LDC in 2000 and 2003 and consist of the following: ABC, Agence France Presse, Associated Press, Cable News Network, MSNBC/NBC, New York Times, Public Radio International, Voice of America and Xinhua News Agency.
Samples
For an example of the data contained in this corpus, please examine this sample file.