Effective string processing and matching for author disambiguation.
Journal
Proceedings of the 2013 KDD Cup 2013 Workshop, KDD Cup 2013, Chicago, Illinois, USA, August 11-14, 2013
Pages
7:1-7:9
Date Issued
2013
Author(s)
Chin, Wei-Sheng et al.
Juan, Yu-Chin
Zhuang, Yong
Wu, Felix
Tung, Hsiao-Yu
Yu, Tong
Wang, Jui-Pin
Chang, Cheng-Xia
Yang, Chun-Pai
Chang, Wei-Cheng
Huang, Kuan-Hao
Kuo, Tzu-Ming
Lin, Shan-Wei
Lin, Young-San
Lu, Yu-Chen
Su, Yu-Chuan
Wei, Cheng-Kuang
Yin, Tu-Chun
Li, Chun-Liang
Lin, Ting-Wei
Tsai, Cheng-Hao
Abstract
Track 2 in KDD Cup 2013 aims at determining duplicated authors in a data set from Microsoft Academic Search. This type of problems appears in many large-scale applications that compile information from different sources. This paper describes our solution developed at National Taiwan University to win the first prize of the competition. We propose an effective name matching framework and realize two implementations. An important strategy in our approach is to consider Chinese and non-Chinese names separately because of their different naming conventions. Post-processing including merging results of two predictions further boosts the performance. Our approach achieves F1-score 0.99202 on the private leader board, while 0.99195 on the public leader board.
SDGs
Type
conference paper