Experimental evaluation reveals improved retrieval performance from text normalization and stemming in Kurdish documents, highlighting core tools for low-resource language processing.
The Kurdish language is an Indo-European language spoken in Kurdistan, a large geographical region in the Middle East. Despite having a large number of speakers, Kurdish is among the less-resourced languages and has not seen much attention from the IR and NLP research communities. This article reports on the outcomes of a project aimed at providing essential resources for processing Kurdish texts. A principal output of this project is Pewan, the first standard Test Collection to evaluate Kurdish Information Retrieval systems. The other language resources that we have built include a lightweight stemmer and a list of stopwords. Our second principal contribution is using these newly-built resources to conduct a thorough experimental study on Kurdish documents. Our experimental results show that normalization, and to a lesser extent, stemming, can greatly improve the performance of Kurdish IR systems.
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Esmaili et al. (2014) studied this question.
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