COUNTER - COrpus of Urdu News TExt Reuse

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-of-the-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

Evaluating text reuse discovery on the web

Text reuse detection aims to identify duplicates, reformulations or partial rewrites of a given text. Some previous research has focused on determining text reuse instances accurately on local corpora. However, the practical usage of finding text reuse on the web has remained largely untested. In this work, we 1) introduce a novel text reuse searching interface for the web, based on a previously proposed architecture, 2) evaluate its feasibility, and 3) investigate techniques to improve both effectiveness and efficiency. Our results show that exhaustive query submission using n-grams can dramatically reduce the execution time with only small losses in accuracy