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Urdu is a widely spoken language with 163 million speakers worldwide acrossthe globe. Information Retrieval (IR) for Urdu entails special consideration ofresearch community due to its rich morphological features and a large number ofspeakers. In general, IR evaluation task is not extensively explored for Urdu.The most important missing element is the availability of a standardizedevaluation corpus specific to Urdu. In this research work, we propose andconstruct a standard test collection of Urdu documents for IR evaluation andnamed it Collection for Urdu Retrieval Evaluation (CURE). We select 1,096unique documents against 50 diverse queries from a large collection of 0.5million crawled documents using two IR models. The purpose of test collectionis the evaluation of IR models, ranking algorithms, and different naturallanguage processing techniques. Next, we perform binary relevance judgment onthe selected documents. We also built two other language resources forlemmatization and query expansion specific to our test collection. Evaluationof test collection is carried out using four retrieval models as well using thestop-words list, lemmatization, and query expansion. Furthermore, erroranalysis was performed for each query with different NLP techniques. To thebest of our knowledge, this work is the first attempt for preparing astandardized information retrieval evaluation test collection for the Urdulanguage.

CURE: Collection for Urdu Information Retrieval Evaluation and Ranking