Named Entity Translation with Web Mining and Transliteration
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1629-1633.
Abstract
This paper presents a novel approach to improve the named entity translation by combining a translite-ration approach with web mining, using web in-formation as a source to complement translitera-tion, and using transliteration information to guide and enhance web mining. A Maximum Entropy model is employed to rank translation candidates by combining pronunciation similarity and bilingual contextual co-occurrence. Experimental results show that our approach effectively improves the precision and recall of the named entity translation by a large margin.
@inproceedings{ijcai2007p263,
title = {Named Entity Translation with Web Mining and Transliteration},
author = {Jiang, Long and Zhou, Ming and Chien, Lee-Feng and Niu, Cheng},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1629--1633},
year = {2007},
url = {https://ijcai.org/proceedings/2007/263},
}
TY - CPAPER TI - Named Entity Translation with Web Mining and Transliteration AU - Jiang, Long AU - Zhou, Ming AU - Chien, Lee-Feng AU - Niu, Cheng T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1629 EP - 1633 UR - https://ijcai.org/proceedings/2007/263 ER -Copied
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