Classification of Jewish Law Articles According to the Ethnic Group of their Writers Using Stems Only

    Abstract

    In this paper, we investigate automatic classification of one thousand Jewish Law articles written in Hebrew-Aramaic according to the ethnic group of their authors. After extracting the stems of the words in each article, the most frequent (>95%) and the least frequent (<5%) stems were removed. Using 480 stems as inputs to an artificial neural network model… (More)

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    Cite this paper

    @inproceedings{ClassificationOJ, title={Classification of Jewish Law Articles According to the Ethnic Group of their Writers Using Stems Only}, author={} }