A New Framework for Personal Name Disambiguation

Lilia Georgieva, Sirisuda Buatongkue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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: (i) identification of referential ambiguity, (ii) identification of lexical ambiguity, and (iii) 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.
Original languageEnglish
Title of host publicationIntelligent Computing
Subtitle of host publicationSAI 2018
Number of pages15
ISBN (Electronic)9783030011741
ISBN (Print)9783030011734
Publication statusPublished - 2 Nov 2018
EventComputing Conference 2018 - London, United Kingdom
Duration: 10 Jul 201812 Jul 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceComputing Conference 2018
CountryUnited Kingdom


  • personal name disambiguation
  • data cleaning
  • lexical ambiguity

ASJC Scopus subject areas

  • Computer Science(all)

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