设计有效的基于web挖掘的面向对象语言翻译技术

Haitao Yu, F. Ren, Degen Huang, Lishuang Li
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引用次数: 1

摘要

由于现有双语词典的覆盖范围有限,在许多自然语言处理任务中,词汇外术语(OOV)的翻译往往很困难。在本文中,我们提出了一种通用的三级级联挖掘技术,它利用OOV类别来优化每一步的有效性。提出了基于OOV分类的扩展策略,以获得更多相关的混合语言文档。提出了基于OOV分类的混合提取方法,实现了鲁棒性提取。提出了一种基于面向对象分类的更灵活的模型组合方法。此外,我们还进行了实验,以评估每个步骤的有效性和采矿技术的整体性能。实验结果表明,与现有方法相比,该方法的性能有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing effective web mining-based techniques for OOV translation
Due to a limited coverage of the existing bilingual dictionary, it is often difficult to translate the Out-Of-Vocabulary terms (OOV) in many natural language processing tasks. In this paper, we propose a general cascade mining technique of three steps, it leverages OOV category to optimize the effectiveness of each step. OOV category based expansion policy is suggested to get more relevant mixed-language documents. OOV category based hybrid extraction approach is suggested to perform a robust extraction. A more flexible model combination based on OOV category is also suggested. Moreover, we conducted experiments to evaluate the effectiveness of each step and the overall performance of the mining technique. The experimental results show significantly performance improvement than the existing methods.
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