文章基本信息

标题：Unsupervised Morphological Relatedness
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作者：Ahmed Khorsi ; Abeer Alsheddi
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：10
DOI：10.14569/IJACSA.2016.071047
出版社：Science and Information Society (SAI)
摘要：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 char-acteristics 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%.
关键词：thesai; IJACSA Volume 7 Issue 10; Arabic Language; Computational Linguistics; Morphological Relatedness; Semitic Morphology; Unsupervised Learning