A Cover Page 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% of the validation examples, is reasonable, considering that the stemming software accuracy is not perfect. Discarding 340 less relevant stems, and retraining with 140 stems gave the same error rate. It seems that classification based on stems only may be suitable for such a classification of other texts. It will be interesting to check whether stylistic classification can be also used for other tasks of ethnic classification, e.g.: various kinds of Muslims that use Arabic.