Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR
Abstract
To support more precise query translation for English-Chinese Bi-Directional Cross-Language Information Retrieval (CLIR), we have developed a novel framework by integrating a semantic network to characterize the correlations between multiple inter-related text terms of interest and learn their inter-related statistical query translation models. First, a semantic network is automatically generated from large-scale English-Chinese bilingual parallel corpora to characterize the correlations between a large number of text terms of interest. Second, the semantic network is exploited to learn the statistical query translation models for such text terms of interest. Finally, these inter-related query translation models are used to translate the queries more precisely and achieve more effective CLIR. Our experiments on a large number of official public data have obtained very positive results.
Cite
Text
Zhang et al. "Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-320Markdown
[Zhang et al. "Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/zhang2011ijcai-learning/) doi:10.5591/978-1-57735-516-8/IJCAI11-320BibTeX
@inproceedings{zhang2011ijcai-learning,
title = {{Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR}},
author = {Zhang, Yuejie and Cen, Lei and Jin, Cheng and Xue, Xiangyang and Fan, Jianping},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1915-1920},
doi = {10.5591/978-1-57735-516-8/IJCAI11-320},
url = {https://mlanthology.org/ijcai/2011/zhang2011ijcai-learning/}
}