Improvements in Analogical Learning: Application to Translating Multi-Terms of the Medical Domain
Improvements in Analogical Learning: Application to Translating Multi-Terms of the Medical Domain
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类比学习的改进：应用于翻译医学领域的多个术语
DOI：
10.3115/1609067.1609121
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Pierre Zweigenbaum
中科院分区：
文献类型：
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作者：
P. Langlais;François Yvon;Pierre Zweigenbaum
术语处理是翻译工作流程中的一个重要问题。然而，目前的机器翻译（MT）系统还没有提出任何积极的工具，协助管理术语数据库。在这项工作中，我们研究了几个增强类比学习和测试我们的实现翻译医学术语。我们表明，类比引擎的工作同样很好地翻译时，从一个形态丰富的语言，或当处理语言对写在不同的脚本。将其与基于短语的统计引擎相结合会带来显著的改进。
Handling terminology is an important matter in a translation workflow. However, current Machine Translation (MT) systems do not yet propose anything proactive upon tools which assist in managing terminological databases. In this work, we investigate several enhancements to analogical learning and test our implementation on translating medical terms. We show that the analogical engine works equally well when translating from and into a morphologically rich language, or when dealing with language pairs written in different scripts. Combining it with a phrase-based statistical engine leads to significant improvements.