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SAI 2018: Intelligent Computing pp 995–1009Cite as

A New Framework for Personal Name Disambiguation

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 858))

Abstract

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.

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Author information

Authors and Affiliations

  1. School of Mathematical and Computer Sciences, Heriot Watt University, Edinburgh, EH14 4AS, UK

    L. Georgieva & S. Buatongkue

Authors
  1. L. Georgieva
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  2. S. Buatongkue
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Corresponding author

Correspondence to L. Georgieva .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Department of Information Science, Saga University, Honjo, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Georgieva, L., Buatongkue, S. (2019). A New Framework for Personal Name Disambiguation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_76

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