| Summary: | 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.
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