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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Unsupervised Morphological Relatedness
Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.071047
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 10, 2016.
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%.
Keywords: Arabic Language; Computational Linguistics; Morphological Relatedness; Semitic Morphology; Unsupervised Learning
Ahmed Khorsi and Abeer Alsheddi, “Unsupervised Morphological Relatedness” International Journal of Advanced Computer Science and Applications(IJACSA), 7(10), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071047
@article{Khorsi2016,
title = {Unsupervised Morphological Relatedness},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071047},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071047},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {10},
author = {Ahmed Khorsi and Abeer Alsheddi}
}
