Unsupervised Morphological Relatedness
نویسندگان
چکیده
Assessment of the similarities between texts has been studied for decades from different perspectives and for several purposes. One interesting perspective is the morphology. This article reports the results on a study on the assessment of the morphological relatedness between natural language words. The main idea is to adapt a formal string alignment algorithm namely Needleman-Wunsch’s to accommodate the statistical characteristics of the words in order to approximate how similar are the linguistic morphologies of the two words. The approach is unsupervised from end to end and the experiments show an nDCG reaching 87% and an r-precision reaching 81%. Keywords—Arabic Language; Computational Linguistics; Morphological Relatedness; Semitic Morphology; Unsupervised Learning
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تاریخ انتشار 2016