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

In this study, we deal with texts in languages (Hebrew/Aramaic) which have been little studied. Moreover, Semitic language processing in general is of great interest today. In particular, we investigate how to classify Jewish Law articles written in Hebrew-Aramaic according to the ethnic group of their authors. The classification is done using only stems of words, excluding very frequent stems and very rare stems. The motivation is to investigate the cultural differences in writing between Ashkenazi authors and Sephardi 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, the classification result, 85 % o