| 基于Map-Reduce的自适应双语短语挖掘系统 |
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| 引用本文： | 李彬,杨世泉,陈文杰.基于Map-Reduce的自适应双语短语挖掘系统[J].昆明师范高等专科学校学报,2013(3):83-87. |
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| 作者姓名： | 李彬 杨世泉 陈文杰 |
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| 作者单位： | [1]中国人民解放军78300部队自动化工作站,云南昆明650032 [2]阿里云计算有限公司,浙江杭州310012 |
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| 摘 要： | 对于跨语言信息检索,统计翻译等应用,双语短语都是极其重要的资源.提出了基于自适应模式的双语短语挖掘算法,该算法可以自动的学习当前Web页面的翻译模式,然后利用学习到的模式抽取当前页面中的双语短语.同时,将自适应双语短语挖掘算法与Map-Reduce并行编程模型融合起来,大大提高了系统的运行效率,并且通过实验验证了该方法的有效性.
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| 关 键 词： | 自适应模式 双语短语 Map-Reduce并行计算框架 分布式计算 |
Map-Reduce Based Adaptive Bilingual Term Mining System |
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| LI Bin,YANG Shi-quan,CHEN Wen-jie.Map-Reduce Based Adaptive Bilingual Term Mining System[J].Journal of Kunming Teachers College,2013(3):83-87. |
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| Authors: | LI Bin YANG Shi-quan CHEN Wen-jie |
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| Institution: | 1 Command Automation Station, Chinese People Liberation Army 78300 Troops, Yunnan Kunming 650032, China; 2. Aliyun Cloud Computing Company, Zhejiang Hangzhou 310012, China) |
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| Abstract: | Bilingual term is critical resource for cross language information retrieval, statistical machine translation etc. In this paper, we presents a new bilingual term mining algorithm which can adaptively learn translation pattern from web page and extract translation pairs with learnt pattern. We also propose a distributed solution which combines adaptive bilingual term mining algorithm and Map- Reduce parallel programming model to improve system performance. Several tests were made to prove that our mining system is effective and efficient. |
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| Keywords: | adaptive pattern bilingual term Map-Reduce parallel programming model distributed computing |
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