ELM-based name disambiguation in bibliography

It is common that different people share the same name. When it occurs in bibliography databases, it worsens the performance of information retrieval and data management. In this paper, we address the problem of name disambiguation and propose two different strategies, one classifier for each name (...

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Bibliographic Details
Published inWorld wide web (Bussum) Vol. 18; no. 2; pp. 253 - 263
Main Authors Han, Donghong, Liu, Siqi, Hu, Yachao, Wang, Bin, Sun, Yongjiao
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.03.2015
Subjects
Bibliographies
Classifiers
Computer Science
Database Management
Information retrieval
Information Systems Applications (incl.Internet)
Learning
Least squares method
Names
Neural networks
Operating Systems
Strategy
Support vector machines
Least square support vector machine
Classifier
Support vector machine
Extreme learning machine
Name disambiguation
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Summary:It is common that different people share the same name. When it occurs in bibliography databases, it worsens the performance of information retrieval and data management. In this paper, we address the problem of name disambiguation and propose two different strategies, one classifier for each name (OCEN) and one classifier for all names (OCAN). Both strategies OCEN and OCAN are based on extreme learning machine (ELM) which shows similar or better generalization performance and faster learning speed than support vector machines (SVM) and least squares support vector machines (LS-SVM). We conduct experiments to compare the performance of ELM, SVM and LS-SVM in the two strategies.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1386-145X
1573-1413
DOI:10.1007/s11280-013-0226-4