TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Malaysian Name-based Ethnicity Classification using LSTM |
| 영문제목(English Title) |
Malaysian Name-based Ethnicity Classification using LSTM |
| 저자(Author) |
Xibin Jia
Zijia Lu
Qing Mi
Zhefeng An
Xiaoyong Li
Min Hong
Youngbum Hur
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| 원문수록처(Citation) |
VOL 16 NO. 12 PP. 3855 ~ 3867 (2022. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Name separation (splitting full names into surnames and given names) is not a tedious task in a multiethnic country because the procedure for splitting surnames and given names is ethnicity-specific. Malaysia has multiple main ethnic groups; therefore, separating Malaysian full names into surnames and given names proves a challenge. In this study, we develop a two-phase framework for Malaysian name separation using deep learning. In the initial phase, we predict the ethnicity of full names. We propose a recurrent neural network with long short-term memory network–based model with character embeddings for prediction. Based on the predicted ethnicity, we use a rule-based algorithm for splitting full names into surnames and given names in the second phase. We evaluate the performance of the proposed model against various machine learning models and demonstrate that it outperforms them by an average of 9%. Moreover, transfer learning and fine-tuning of the proposed model with an additional dataset results in an improvement of up to 7% on average. |
| 키워드(Keyword) |
graph deep clustering
heterogeneous information networks
representation learning
student behavior modeling
Deep Learning
Recurrent Neural Network
LSTM
Machine Learning
Ethnicity Classification
Malaysian Name Separation
Deep Learning-based Name Separation
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