利用实体链接和相关语言投射来提高姓名音译

NEWS@ACM Pub Date : 1900-01-01 DOI:10.18653/v1/W16-2701
Ying Lin, Xiaoman Pan, Aliya Deri, Heng Ji, Kevin Knight
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引用次数: 13

摘要

传统的名称音译方法在消除实体歧义时,很大程度上忽略了源上下文信息和实体之间的相互依赖关系。我们提出了一种新的方法,利用最先进的实体链接(EL)技术,使用来自源上下文的集体推理和来自知识库的额外证据,自动纠正名称音译结果。将七种语言的名字音译为英语的实验表明,我们的方法比基线模型获得了2.6%到15.7%的绝对增益,并且显著提高了最先进的技术水平。当上下文信息存在时,我们的方法可以通过集体音译和消除多个相关实体的歧义来获得进一步的收益(24.2%)。我们还证明,结合实体链接并从相关语言中投射资源的方法与在原始语言中使用相同数量的训练对而不使用实体链接的方法获得了相当的性能
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration
Traditional name transliteration methods largely ignore source context information and inter-dependency among entities for entity disambiguation. We propose a novel approach to leverage state-of-the-art Entity Linking (EL) techniques to automatically correct name transliteration results, using collective inference from source contexts and additional evidence from knowledge base. Experiments on transliterating names from seven languages to English demonstrate that our approach achieves 2.6% to 15.7% absolute gain over the baseline model, and significantly advances state-of-the-art. When contextual information exists, our approach can achieve further gains (24.2%) by collectively transliterating and disambiguating multiple related entities. We also prove that combining Entity Linking and projecting resources from related languages obtained comparable performance as themethod using the same amount of training pairs in the original languageswithout Entity Linking.1
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