Named Entity Translation with Web Mining and Transliteration
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.
Cite
Text
Jiang et al. "Named Entity Translation with Web Mining and Transliteration." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Jiang et al. "Named Entity Translation with Web Mining and Transliteration." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/jiang2007ijcai-named/)BibTeX
@inproceedings{jiang2007ijcai-named,
title = {{Named Entity Translation with Web Mining and Transliteration}},
author = {Jiang, Long and Zhou, Ming and Chien, Lee-Feng and Niu, Cheng},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {1629-1634},
url = {https://mlanthology.org/ijcai/2007/jiang2007ijcai-named/}
}