Unsupervised web name disambiguation using semantic similarity and single-pass clustering

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a method for name disambiguation. For a given set of names and documents we cluster the documents and map each cluster to the appropriate name. The proposed method incorporates an unsupervised metric for semantic similarity computation and a computationally low-cost clustering algorithm. We experimented with the data used in Web People Search Task of SemEval-2007, in which 16 different teams were participated. The proposed system has an equal performance compared to the officially best system.

Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publicationTheories, Models and Applications - 6th Hellenic Conference on AI, SETN 2010, Proceedings
Pages133-141
Number of pages9
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications, SETN 2010 - Athens, Greece
Duration: 4 May 20107 May 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6040 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications, SETN 2010
Country/TerritoryGreece
CityAthens
Period4/05/107/05/10

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