Browsing by Author "Eghbalzadeh, H."

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    Persica: A Persian corpus for multi-purpose text mining and natural language processing
    (IEEE, 2012) Eghbalzadeh, H.; Hosseini, B; Khadivi, S.; Khodabakhsh, Ali; Khodabakhsh, Ali
    Lack of multi-application text corpus despite of the surging text data is a serious bottleneck in the text mining and natural language processing especially in Persian language. This paper presents a new corpus for NEWS articles analysis in Persian called Persica. NEWS analysis includes NEWS classification, topic discovery and classification, trend discovery, category classification and many more procedures. Dealing with NEWS has special requirements. First of all it needs a valid and NEWS-content-enriched corpus to perform the experiments. Our Approach is based on a modified category classification and data normalization over Persian NEWS articles which has led to creation of a multipurpose Persian corpus which shows reasonable results in text mining outcomes. In the literature, regarding to our knowledge there are few Persian corpuses but none of them have Persian NEWS time trend characteristics. Empirical results on our benchmark indicate that in addition to reducing the problem dimensions and useless content, Persica keeps admissible validity and reliability in comparison with standard corpuses in the literature.