Unsupervised Morphological Relatedness
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 10
Abstract
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%.
Authors and Affiliations
Ahmed Khorsi, Abeer Alsheddi
Keywords
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- EP ID EP91026
- DOI 10.14569/IJACSA.2016.071047
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