AHP based feature ranking model using string similarity for resolving name ambiguity

	AHP based feature ranking model using string similarity for resolving name ambiguity
International Journal of Nonlinear Analysis and Applications
دوره 12، Special Issue، اسفند 2021، صفحه 1745-1751 اصل مقاله (635.33 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5862
نویسندگان
M. Subathra^* ؛ V. Umarani
Department of Computer Applications, PSG College of Technology, Coimbatore, Tamilnadu, India
چکیده
In recent years of Natural Language Processing research, the name ambiguity problem remains unresolved while retrieving the information of author names from bibliographic citations in a digital library system. In this paper, a feature ranking model is investigated that resolve the ambiguity problem with Analytical Hierarchy Process (AHP). The AHP procedure prioritizes and assigns the weights for certain criteria which forms a judgemental matrix called pairwise comparison matrix. The result of the AHP analysis aims to get the preprocessing level using Levenshtein Distance. Finally, the AHP helps to find the co-author criteria as the highest priority than the other criteria taken from the digital library data set.
کلیدواژه‌ها
NLP؛ citations؛ digital library؛ Levenshtein distance؛ AHP
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