In this paper, we study the problem of personal name disambiguation (NED). We develop a framework to address the three challenges in personal name disambiguation: (1) identification of referential ambiguity; (2) identification of lexical ambiguity; and (3) predicting the NIL value, that is the value when a named entity cannot be mapped to a knowledge base. Our framework includes extractor, searcher and disambiguator. Experimental results evaluated on real-world data sets show that our framework and algorithm provide accuracy in personal name linking up to 92%, which is higher than the accuracy of previously developed algorithms.
CITATION STYLE
Georgieva, L., & Buatongkue, S. (2019). A new framework for personal name disambiguation. In Advances in Intelligent Systems and Computing (Vol. 858, pp. 995–1009). Springer Verlag. https://doi.org/10.1007/978-3-030-01174-1_76
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