@inproceedings{gao2001improving,
author = {Gao, Jianfeng and Nie, Jian-Yun and Xun, Endong and Zhang, Jian and Zhou, Ming and Huang, Changning},
title = {Improving Query Translation for CLIR Using Statistical Models},
booktitle = {SIGIR'01, New Orleans, Lousiana, USA},
year = {2001},
month = {September},
abstract = {Dictionaries have often been used for query translation in crosslanguage information retrieval (CLIR). However, we are faced with the problem of translation ambiguity, i.e. multiple translations are stored in a dictionary for a word. In addition, a word-by-word query translation is not precise enough. In this paper, we explore several methods to improve the previous dictionary-based query translation. First, as many as possible, noun phrases are recognized and translated as a whole by using statistical models and phrase translation patterns. Second, the best word translations are selected based on the cohesion of the translation words. Our experimental results on TREC English-Chinese CLIR collection show that these techniques result in significant improvements over the simple dictionary approaches, and achieve even better performance than a high-quality machine translation system.},
url = {http://approjects.co.za/?big=en-us/research/publication/improving-query-translation-clir-using-statistical-models/},
edition = {SIGIR'01, New Orleans, Lousiana, USA},
}