A New Framework for Personal Name Disambiguation

Lilia Georgieva, Sirisuda Buatongkue

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

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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: (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
PublisherSpringer
Pages995-1009
Number of pages15
ISBN (Electronic)9783030011741
ISBN (Print)9783030011734
DOIs
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
PublisherSpringer
Volume858
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceComputing Conference 2018
CountryUnited Kingdom
CityLondon
Period10/07/1812/07/18

Keywords

  • personal name disambiguation
  • data cleaning
  • lexical ambiguity

ASJC Scopus subject areas

  • Computer Science(all)

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