Text reuse is the act of borrowing text from existing documents to create new texts. Freely available and easily accessible large online repositories are not only making reuse of text more common in society but also harder to detect. A major hindrance in the development and evaluation of existing/new mono-lingual text reuse detection methods, especially for South Asian languages, is the unavailability of standardized benchmark corpora. Amongst other things, a gold standard corpus enables researchers to directly compare existing state-ofthe-art methods. In our study, we address this gap by developing a benchmark corpus for one of the widely spoken but under resourced languages i.e. Urdu. The COUNTER (COrpus of Urdu News TExt Reuse) corpus contains 1,200 documents with real examples of text reuse from the field of journalism. It has been manually annotated at document level with three levels of reuse: wholly derived, partially derived and non derived. We also apply a number of similarity estimation methods on our corpus to show how it can be used for the development, evaluation and comparison of text reuse detection systems for the Urdu language. The corpus is a vital resource for the development and evaluation of text reuse detection systems in general and specifically for Urdu language.
COUNTER, the COrpus of Urdu News TExt Reuse REF is the first benchmark dataset developed with an intention to foster text reuse detection research in the Urdu language.
COUNTER: corpus of Urdu news text reuse
Language Resources and Evaluation
Computer Science