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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 | ||
| تاریخ دریافت: 18 مهر 1400، تاریخ بازنگری: 11 آبان 1400، تاریخ پذیرش: 03 آذر 1400 | ||
| 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 | ||
| مراجع | ||
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