Abstract
Personal names tend to have many variations differing from country to country. Though there exists a large amount of personal names on the Web, nationality prediction solely based on names has not been fully studied due to its difficulties in extracting subtle character level features. We propose a recurrent neural network based model which predicts nationalities of each name using automatic feature extraction. Evaluation of Olympic record data shows that our model achieves greater accuracy than previous feature based approaches in nationality prediction tasks. We also evaluate our proposed model and baseline models on name ethnicity classification task, again achieving better or comparable performances. We further investigate the effectiveness of character embeddings used in our proposed model.
Original language | English |
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Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
Editors | Carles Sierra |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 2081-2087 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241103 |
DOIs | |
Publication status | Published - 2017 |
Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 2017 Aug 19 → 2017 Aug 25 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Volume | 0 |
ISSN (Print) | 1045-0823 |
Other
Other | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
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Country/Territory | Australia |
City | Melbourne |
Period | 17/8/19 → 17/8/25 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grant funded by the government of Korea (MSIP) (NRF-2014R1A2A1A10051238).
ASJC Scopus subject areas
- Artificial Intelligence