TELKOMNIKA Telecommunication, Computing, Electronics and Control
Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries
Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries
Subject
Author name disambiguation
Bibliographic data
Deep neural networks
Homonym
Symonym
Bibliographic data
Deep neural networks
Homonym
Symonym
Description
Author name disambiguation (AND), also recognized as name-identification,
has long been seen as a challenging issue in bibliographic data. In other words,
the same author may appear under separate names, synonyms, or distinct
authors may have similar to those referred to as homonyms. Some previous
research has proposed AND problem. To the best of our knowledge, no study
discussed specifically synonym and homonym, whereas such cases are the
core in AND topic. This paper presents the classification of non-homonym-
synonym, homonym-synonym, synonym, and homonym cases by using the
DBLP computer science bibliography dataset. Based on the DBLP raw data,
the classification process is proposed by using deep neural networks (DNNs).
In the classification process, the DBLP raw data divided into five features,
including name, author, title, venue, and year. Twelve scenarios are designed
with a different structure to validate and select the best model of DNNs.
Furthermore, this paper is also compared DNNs with other classifiers, such as
support vector machine (SVM) and decision tree. The results show DNNs
outperform SVM and decision tree methods in all performance metrics. The
DNNs performances with three hidden layers as the best model, achieve
accuracy, sensitivity, specificity, precision, and F1-score are 98.85%, 95.95%,
99.26%, 94.80%, and 95.36%, respectively. In the future, DNNs are more
performing with the automated feature representation in AND processing.
has long been seen as a challenging issue in bibliographic data. In other words,
the same author may appear under separate names, synonyms, or distinct
authors may have similar to those referred to as homonyms. Some previous
research has proposed AND problem. To the best of our knowledge, no study
discussed specifically synonym and homonym, whereas such cases are the
core in AND topic. This paper presents the classification of non-homonym-
synonym, homonym-synonym, synonym, and homonym cases by using the
DBLP computer science bibliography dataset. Based on the DBLP raw data,
the classification process is proposed by using deep neural networks (DNNs).
In the classification process, the DBLP raw data divided into five features,
including name, author, title, venue, and year. Twelve scenarios are designed
with a different structure to validate and select the best model of DNNs.
Furthermore, this paper is also compared DNNs with other classifiers, such as
support vector machine (SVM) and decision tree. The results show DNNs
outperform SVM and decision tree methods in all performance metrics. The
DNNs performances with three hidden layers as the best model, achieve
accuracy, sensitivity, specificity, precision, and F1-score are 98.85%, 95.95%,
99.26%, 94.80%, and 95.36%, respectively. In the future, DNNs are more
performing with the automated feature representation in AND processing.
Creator
Firdaus, Siti Nurmaini, Varindo Ockta Keneddi Putra, Annisa Darmawahyuni, Reza FirsandayaMalik, Muhammad Naufal Rachmatullah, Andre Herviant Juliano, Tio Artha Nugraha
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Jan 20, 2021
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Firdaus, Siti Nurmaini, Varindo Ockta Keneddi Putra, Annisa Darmawahyuni, Reza FirsandayaMalik, Muhammad Naufal Rachmatullah, Andre Herviant Juliano, Tio Artha Nugraha, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4099.
Neural network technique with deep structure for improving author homonym and synonym classification in digital libraries,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4099.