---
title: "Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR"
authors:
  - "Yuejie Zhang"
  - "Lei Cen"
  - "Cheng Jin"
  - "Xiangyang Xue"
  - "Jianping Fan"
doi: "10.5591/978-1-57735-516-8/IJCAI11-320"
url: "https://ijcai.org/proceedings/2011/320"
pdf: "https://ijcai.org/proceedings/2011/0320.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1915-1919"
track: ""
keywords:
---

# Learning Inter-Related Statistical Query Translation Models for English-Chinese Bi-Directional CLIR

Yuejie Zhang, Lei Cen, Cheng Jin, Xiangyang Xue, Jianping Fan

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1915-1919. DOI: [10.5591/978-1-57735-516-8/IJCAI11-320](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-320)

## 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.